ModelCheckpoint broadcast fails on multiple GPUs

Nobody has claimed this yet.

### Bug description

When training on 4 A100, when reaching on_train_epoch_end (no custom implementation), the ModelCheckpoint callback is invoked.
Inside the callback, the logger (?) tries to broadcast the weights (?) to all devices.

I was able to trace the issue to torch.distributed.distributed_c10d.py, lines 3382-3504.
Line 3490 tries to allocate a tensor with object_sizes_tensor, but the rank != src, so object_sizes_tensor is init to a big random number, and object_tensor is init with this hugh integer.

Versions:
pytorch 2.6.0
lightning 2.5.0

### What version are you seeing the problem on?

v2.5

### How to reproduce the bug

```python

```

### Error messages and logs

Error 1:
```
[rank1]: Traceback (most recent call last):
[rank1]: File "/opt/conda/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank1]: return _run_code(code, main_globals, None,
[rank1]: File "/opt/conda/lib/python3.10/runpy.py", line 86, in _run_code
[rank1]: exec(code, run_globals)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/menta/runner.py", line 459, in
[rank1]: main()
[rank1]: File "/opt/conda/lib/python3.10/site-packages/menta/runner.py", line 455, in main
[rank1]: local_runner.run()
[rank1]: File "/opt/conda/lib/python3.10/site-packages/menta/runner.py", line 369, in run
[rank1]: trainer.fit(model=module, datamodule=datamodule, ckpt_path=latest_checkpoint_path)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 539, in fit
[rank1]: call._call_and_handle_interrupt(
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 46, in _call_and_handle_interrupt
[rank1]: return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/strategies/launchers/subprocess_script.py", line 105, in launch
[rank1]: return function(*args, **kwargs)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 575, in _fit_impl
[rank1]: self._run(model, ckpt_path=ckpt_path)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 982, in _run
[rank1]: results = self._run_stage()
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/trainer/trainer.py", line 1026, in _run_stage
[rank1]: self.fit_loop.run()
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py", line 217, in run
[rank1]: self.on_advance_end()
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py", line 470, in on_advance_end
[rank1]: call._call_callback_hooks(trainer, "on_train_epoch_end", monitoring_callbacks=True)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py", line 222, in _call_callback_hooks
[rank1]: fn(trainer, trainer.lightning_module, *args, **kwargs)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 325, in on_train_epoch_end
[rank1]: self._save_topk_checkpoint(trainer, monitor_candidates)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 387, in _save_topk_checkpoint
[rank1]: self._save_none_monitor_checkpoint(trainer, monitor_candidates)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 712, in _save_none_monitor_checkpoint
[rank1]: filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, self.best_model_path)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 663, in _get_metric_interpolated_filepath_name
[rank1]: while self.file_exists(filepath, trainer) and filepath != del_filepath:
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 776, in file_exists
[rank1]: return trainer.strategy.broadcast(exists)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/strategies/ddp.py", line 307, in broadcast
[rank1]: torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 83, in wrapper
[rank1]: return func(*args, **kwargs)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3140, in broadcast_object_list
[rank1]: object_tensor = torch.empty( # type: ignore[call-overload]
[rank1]: RuntimeError: /opt/conda/conda-bld/pytorch_1729647352509/work/build/aten/src/ATen/RegisterCUDA.cpp:7275: SymIntArrayRef expected to contain only concrete integers
```

Error 2:
```
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 325, in on_train_epoch_end
[rank1]: self._save_topk_checkpoint(trainer, monitor_candidates)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 387, in _save_topk_checkpoint
[rank1]: self._save_none_monitor_checkpoint(trainer, monitor_candidates)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 712, in _save_none_monitor_checkpoint
[rank1]: filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, self.best_model_path)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 663, in _get_metric_interpolated_filepath_name
[rank1]: while self.file_exists(filepath, trainer) and filepath != del_filepath:
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py", line 776, in file_exists
[rank1]: return trainer.strategy.broadcast(exists)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/lightning/pytorch/strategies/ddp.py", line 307, in broadcast
[rank1]: torch.distributed.broadcast_object_list(obj, src, group=_group.WORLD)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 83, in wrapper
[rank1]: return func(*args, **kwargs)
[rank1]: File "/opt/conda/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3140, in broadcast_object_list
[rank1]: object_tensor = torch.empty( # type: ignore[call-overload]
[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate more than 1EB memory.
```

### Environment

Current environment

* CUDA:
- GPU:
- NVIDIA A100-SXM4-80GB
- NVIDIA A100-SXM4-80GB
- NVIDIA A100-SXM4-80GB
- NVIDIA A100-SXM4-80GB
- available: True
- version: 12.4
* Lightning:
- lightning: 2.5.0.post0
- lightning-utilities: 0.12.0
- pytorch-lightning: 2.5.0.post0
- sagemaker-pytorch-training: 2.8.1
- torch: 2.6.0
- torchaudio: 2.5.1
- torchmetrics: 1.6.1
- torchvision: 0.20.1
* Packages:
- absl-py: 2.1.0
- aiobotocore: 2.19.0
- aiohappyeyeballs: 2.4.6
- aiohttp: 3.11.12
- aioitertools: 0.12.0
- aiosignal: 1.3.2
- angie-data-reader: 0.10.0
- angie-shuffle-service: 0.18.1
- annotated-types: 0.7.0
- antlr4-python3-runtime: 4.9.3
- anyio: 4.8.0
- argon2-cffi: 23.1.0
- argon2-cffi-bindings: 21.2.0
- arrow: 1.3.0
- asttokens: 3.0.0
- async-lru: 2.0.4
- async-timeout: 5.0.1
- attrs: 23.2.0
- autocommand: 2.2.2
- babel: 2.17.0
- backports.tarfile: 1.2.0
- bcrypt: 4.2.1
- beartype: 0.19.0
- beautifulsoup4: 4.13.3
- bleach: 6.2.0
- blinker: 1.9.0
- boto3: 1.36.3
- botocore: 1.36.3
- brotli: 1.1.0
- cached-property: 1.5.2
- cachetools: 5.5.1
- certifi: 2025.1.31
- cffi: 1.17.1
- charset-normalizer: 3.4.1
- click: 8.1.8
- cloud-logging: 0.5.53
- cloud-storage-utils: 1.0.63
- cloudpickle: 2.2.1
- colorama: 0.4.6
- comet-ml: 3.49.1
- comm: 0.2.2
- configobj: 5.0.9
- contextlib2: 21.6.0
- contourpy: 1.3.1
- cryptography: 42.0.8
- cycler: 0.12.1
- cython: 3.0.12
- debugpy: 1.8.12
- decorator: 5.1.1
- defusedxml: 0.7.1
- dill: 0.3.9
- dl-optimizer: 25.10218.0
- dl-optimizer-common: 25.10218.0
- docker: 7.1.0
- dulwich: 0.22.7
- durationpy: 0.9
- einops: 0.8.1
- everett: 3.1.0
- exceptiongroup: 1.2.2
- execnet: 2.1.1
- executing: 2.1.0
- faker: 36.1.1
- fastjsonschema: 2.21.1
- filelock: 3.17.0
- fonttools: 4.56.0
- fpdf: 1.7.2
- fqdn: 1.5.1
- frozenlist: 1.5.0
- fsspec: 2025.2.0
- future: 1.0.0
- getdaft: 0.3.0.dev0
- gevent: 24.11.1
- gmpy2: 2.1.5
- google-api-core: 2.24.1
- google-api-python-client: 2.161.0
- google-auth: 2.38.0
- google-auth-httplib2: 0.2.0
- google-cloud-container: 2.56.0
- google-cloud-core: 2.4.1
- google-cloud-secret-manager: 2.23.0
- google-cloud-storage: 3.0.0
- google-crc32c: 1.1.2
- google-pasta: 0.2.0
- google-resumable-media: 2.7.2
- googleapis-common-protos: 1.67.0
- greenlet: 3.1.1
- grpc-google-iam-v1: 0.14.0
- grpcio: 1.62.2
- grpcio-status: 1.62.2
- h11: 0.14.0
- h2: 4.2.0
- hera: 5.18.0
- hpack: 4.1.0
- httpcore: 1.0.7
- httplib2: 0.22.0
- httpx: 0.28.1
- huggingface-hub: 0.29.0
- hydra-core: 1.3.2
- hyperframe: 6.1.0
- hyperopt: 0.2.7
- idna: 3.10
- importlib-metadata: 6.10.0
- importlib-resources: 6.5.2
- inflect: 7.3.1
- iniconfig: 2.0.0
- inotify-simple: 1.2.1
- ipykernel: 6.29.5
- ipython: 8.32.0
- ipywidgets: 8.1.5
- isoduration: 20.11.0
- jaraco.collections: 5.1.0
- jaraco.context: 5.3.0
- jaraco.functools: 4.0.1
- jaraco.text: 3.12.1
- jedi: 0.19.2
- jinja2: 3.1.5
- jmespath: 1.0.1
- joblib: 1.4.2
- json5: 0.10.0
- jsonpointer: 3.0.0
- jsonschema: 4.23.0
- jsonschema-specifications: 2024.10.1
- jupyter-client: 8.6.3
- jupyter-core: 5.7.2
- jupyter-events: 0.12.0
- jupyter-lsp: 2.2.5
- jupyter-server: 2.15.0
- jupyter-server-terminals: 0.5.3
- jupyterlab: 4.3.5
- jupyterlab-pygments: 0.3.0
- jupyterlab-server: 2.27.3
- jupyterlab-widgets: 3.0.13
- kiwisolver: 1.4.7
- kubernetes: 32.0.1
- lightning: 2.5.0.post0
- lightning-utilities: 0.12.0
- lxml: 5.3.1
- lz4: 4.3.3
- markdown: 3.6
- markdown-it-py: 3.0.0
- markupsafe: 3.0.2
- matplotlib: 3.10.0
- matplotlib-inline: 0.1.7
- mdurl: 0.1.2
- me-auth-client: 0.34.0
- menta3: 0.32.9
- mistune: 3.1.2
- mobileye-pyq: 0.4.0
- mobileye.cloud-users: 0.2.6
- mobileye.metal: 4.2.6
- mobileye.nebula-tools: 2.0.12
- more-itertools: 10.3.0
- mpmath: 1.3.0
- msal: 1.31.1
- multidict: 6.1.0
- multiprocess: 0.70.17
- munkres: 1.1.4
- nbclient: 0.10.2
- nbconvert: 7.16.6
- nbformat: 5.10.4
- nest-asyncio: 1.6.0
- networkx: 3.4.2
- notebook: 7.3.2
- notebook-shim: 0.2.4
- nssd-errors: 1.6.8
- numpy: 1.26.4
- nvidia-cublas-cu12: 12.4.5.8
- nvidia-cuda-cupti-cu12: 12.4.127
- nvidia-cuda-nvrtc-cu12: 12.4.127
- nvidia-cuda-runtime-cu12: 12.4.127
- nvidia-cudnn-cu12: 9.1.0.70
- nvidia-cufft-cu12: 11.2.1.3
- nvidia-curand-cu12: 10.3.5.147
- nvidia-cusolver-cu12: 11.6.1.9
- nvidia-cusparse-cu12: 12.3.1.170
- nvidia-cusparselt-cu12: 0.6.2
- nvidia-nccl-cu12: 2.21.5
- nvidia-nvjitlink-cu12: 12.4.127
- nvidia-nvtx-cu12: 12.4.127
- oauthlib: 3.2.2
- omegaconf: 2.3.0
- opencv-python: 4.11.0.86
- overrides: 7.7.0
- packaging: 24.2
- pandas: 2.2.3
- pandocfilters: 1.5.0
- paramiko: 3.5.1
- parso: 0.8.4
- pathos: 0.3.3
- pexpect: 4.9.0
- pickleshare: 0.7.5
- pillow: 11.1.0
- pip: 25.0.1
- pkgutil-resolve-name: 1.3.10
- platformdirs: 4.3.6
- pluggy: 1.5.0
- pox: 0.3.5
- ppft: 1.7.6.9
- prometheus-client: 0.21.1
- prompt-toolkit: 3.0.50
- propcache: 0.2.1
- proto-plus: 1.26.0
- protobuf: 3.20.3
- psutil: 6.1.1
- ptyprocess: 0.7.0
- pure-eval: 0.2.3
- py4j: 0.10.9.9
- pyarrow: 16.1.0
- pyasn1: 0.6.1
- pyasn1-modules: 0.4.1
- pycocotools: 2.0.8
- pycparser: 2.22
- pydantic: 2.10.6
- pydantic-core: 2.27.2
- pydot: 3.0.4
- pygments: 2.19.1
- pyjwt: 2.10.1
- pynacl: 1.5.0
- pynamodb: 5.5.1
- pyopenssl: 25.0.0
- pyparsing: 3.2.1
- pyside6: 6.8.2
- pysocks: 1.7.1
- pytest: 7.4.4
- pytest-mock: 3.14.0
- pytest-xdist: 3.6.1
- python-box: 6.1.0
- python-dateutil: 2.9.0.post0
- python-json-logger: 2.0.7
- pytorch-lightning: 2.5.0.post0
- pytz: 2024.1
- pyu2f: 0.1.5
- pyyaml: 6.0.2
- pyzmq: 26.2.1
- referencing: 0.36.2
- requests: 2.32.3
- requests-oauthlib: 2.0.0
- requests-toolbelt: 1.0.0
- retrying: 1.3.4
- rfc3339-validator: 0.1.4
- rfc3986-validator: 0.1.1
- rich: 13.9.4
- rpds-py: 0.22.3
- rsa: 4.9
- ruamel.yaml: 0.18.10
- ruamel.yaml.clib: 0.2.8
- s3fs: 2025.2.0
- s3path: 0.6.0
- s3transfer: 0.11.2
- safetensors: 0.5.2
- sagemaker: 2.210.0
- sagemaker-pytorch-training: 2.8.1
- sagemaker-training: 4.9.0
- schema: 0.7.7
- scikit-learn: 1.6.1
- scipy: 1.15.2
- semantic-version: 2.10.0
- send2trash: 1.8.3
- sentry-sdk: 2.22.0
- setuptools: 75.8.0
- shiboken6: 6.8.2
- six: 1.17.0
- smart-open: 7.1.0
- smdebug-rulesconfig: 1.0.1
- sniffio: 1.3.1
- soupsieve: 2.5
- stack-data: 0.6.3
- sympy: 1.13.1
- tabulate: 0.9.0
- tblib: 2.0.0
- tenacity: 9.0.0
- tensorboard: 2.18.0
- tensorboard-data-server: 0.7.0
- termcolor: 2.5.0
- terminado: 0.18.1
- threadpoolctl: 3.5.0
- timm: 1.0.14
- tinycss2: 1.4.0
- toml: 0.10.2
- tomli: 2.2.1
- torch: 2.6.0
- torchaudio: 2.5.1
- torchmetrics: 1.6.1
- torchvision: 0.20.1
- tornado: 6.4.2
- tqdm: 4.67.1
- traitlets: 5.14.3
- triton: 3.2.0
- typeguard: 4.3.0
- types-python-dateutil: 2.9.0.20241206
- typing-extensions: 4.12.2
- typing-utils: 0.1.0
- tzdata: 2025.1
- unicodedata2: 16.0.0
- uri-template: 1.3.0
- uritemplate: 4.1.1
- urllib3: 1.26.19
- wcwidth: 0.2.13
- webcolors: 24.11.1
- webencodings: 0.5.1
- websocket-client: 1.8.0
- werkzeug: 3.1.3
- wheel: 0.45.1
- widgetsnbextension: 4.0.13
- wrapt: 1.17.2
- wurlitzer: 3.1.1
- yarl: 1.18.3
- zipp: 3.21.0
- zope.event: 5.0
- zope.interface: 7.2
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.10.16
- release: 5.15.0-105-generic
- version: #115-Ubuntu SMP Mon Apr 15 09:52:04 UTC 2024

### More info

_No response_

cc @justusschock

Start with lightning/pytorch/callbacks/model_checkpoint.py at file_exists and lightning/pytorch/strategies/ddp.py at broadcast, then trace the reported torch.distributed.broadcast_object_list path. Done means ModelCheckpoint completes on the reported four-GPU setup without the invalid-size or huge-allocation errors, with coverage for the failure if the project’s tests support it.

Written by the indexing model from the issue text.