Cannot import name 'default_pg_timeout' from 'torch.distributed'

[ayushnoori]

Bug description

Hi there,

I'm attempting to install PyTorch Lightning on an Apple MacBook Pro with M1 Max Chip. I appeared to successfully install Lightning via:

pip install pytorch-lightning  

However, when I try to actually load the package, I receive the following error message (where {USER_NAME} is my user name and {ENV} is the name of my environment):

>>> import pytorch_lightning as pl
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/pytorch_lightning/__init__.py", line 34, in <module>
    from lightning_fabric.utilities.seed import seed_everything  # noqa: E402
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/lightning_fabric/__init__.py", line 23, in <module>
    from lightning_fabric.fabric import Fabric  # noqa: E402
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/lightning_fabric/fabric.py", line 34, in <module>
    from lightning_fabric.connector import _Connector, _PLUGIN_INPUT, _PRECISION_INPUT
  File "/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/lightning_fabric/connector.py", line 45, in <module>
    from lightning_fabric.strategies import (
  File "/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/lightning_fabric/strategies/__init__.py", line 17, in <module>
    from lightning_fabric.strategies.fsdp import FSDPStrategy  # noqa: F401
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/lightning_fabric/strategies/fsdp.py", line 21, in <module>
    from torch.distributed import default_pg_timeout
ImportError: cannot import name 'default_pg_timeout' from 'torch.distributed' (/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/torch/distributed/__init__.py)

Indeed, when I trace back to torch.distributed, the following also throws an error:

>>> from torch.distributed import default_pg_timeout
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name 'default_pg_timeout' from 'torch.distributed' (/Users/{USER_NAME}/miniforge3/envs/{ENV}/lib/python3.11/site-packages/torch/distributed/__init__.py)

There doesn't seem to be much available to shed light on this error or why the import fails. I'd be grateful for any assistance to help resolve. Thanks!

How to reproduce the bug

No response

Error messages and logs

# Error messages and logs here please

Environment

• CUDA:
  - GPU: None
  - available: False
  - version: None
• Lightning:
  - lightning-utilities: 0.6.0.post0
  - pytorch-lightning: 1.9.0
  - torch: 1.13.1
  - torch-geometric: 2.2.0
  - torch-scatter: 2.1.0
  - torch-sparse: 0.6.16
  - torchmetrics: 0.11.1
  - torchvision: 0.2.2
• Packages:
  - aiohttp: 3.8.3
  - aiosignal: 1.3.1
  - anyio: 3.6.2
  - appnope: 0.1.3
  - argon2-cffi: 21.3.0
  - argon2-cffi-bindings: 21.2.0
  - asttokens: 2.2.1
  - async-timeout: 4.0.2
  - attrs: 22.2.0
  - babel: 2.11.0
  - backcall: 0.2.0
  - backports.functools-lru-cache: 1.6.4
  - beautifulsoup4: 4.11.2
  - bleach: 6.0.0
  - brotlipy: 0.7.0
  - certifi: 2022.12.7
  - cffi: 1.15.1
  - charset-normalizer: 2.1.1
  - comm: 0.1.2
  - cryptography: 39.0.0
  - debugpy: 1.6.6
  - decorator: 5.1.1
  - defusedxml: 0.7.1
  - entrypoints: 0.4
  - executing: 1.2.0
  - fastjsonschema: 2.16.2
  - flit-core: 3.8.0
  - frozenlist: 1.3.3
  - fsspec: 2023.1.0
  - idna: 3.4
  - importlib-metadata: 6.0.0
  - importlib-resources: 5.10.2
  - ipykernel: 6.21.1
  - ipython: 8.9.0
  - ipython-genutils: 0.2.0
  - ipywidgets: 8.0.4
  - jedi: 0.18.2
  - jinja2: 3.1.2
  - joblib: 1.2.0
  - json5: 0.9.5
  - jsonschema: 4.17.3
  - jupyter: 1.0.0
  - jupyter-client: 8.0.2
  - jupyter-console: 6.4.4
  - jupyter-core: 5.2.0
  - jupyter-events: 0.6.3
  - jupyter-server: 2.2.1
  - jupyter-server-terminals: 0.4.4
  - jupyterlab: 3.5.3
  - jupyterlab-pygments: 0.2.2
  - jupyterlab-server: 2.19.0
  - jupyterlab-widgets: 3.0.5
  - lightning-utilities: 0.6.0.post0
  - markupsafe: 2.1.2
  - matplotlib-inline: 0.1.6
  - mistune: 2.0.4
  - multidict: 6.0.4
  - nbclassic: 0.5.1
  - nbclient: 0.7.2
  - nbconvert: 7.2.9
  - nbformat: 5.7.3
  - nest-asyncio: 1.5.6
  - notebook: 6.5.2
  - notebook-shim: 0.2.2
  - numpy: 1.24.1
  - packaging: 23.0
  - pandocfilters: 1.5.0
  - parso: 0.8.3
  - pexpect: 4.8.0
  - pickleshare: 0.7.5
  - pillow: 9.4.0
  - pip: 23.0
  - pkgutil-resolve-name: 1.3.10
  - platformdirs: 2.6.2
  - prometheus-client: 0.16.0
  - prompt-toolkit: 3.0.36
  - psutil: 5.9.4
  - ptyprocess: 0.7.0
  - pure-eval: 0.2.2
  - pycparser: 2.21
  - pygments: 2.14.0
  - pyopenssl: 23.0.0
  - pyparsing: 3.0.9
  - pyrsistent: 0.19.3
  - pysocks: 1.7.1
  - python-dateutil: 2.8.2
  - python-json-logger: 2.0.4
  - pytorch-lightning: 1.9.0
  - pytz: 2022.7.1
  - pyyaml: 6.0
  - pyzmq: 25.0.0
  - requests: 2.28.2
  - rfc3339-validator: 0.1.4
  - rfc3986-validator: 0.1.1
  - scikit-learn: 1.2.1
  - scipy: 1.10.0
  - send2trash: 1.8.0
  - setuptools: 67.1.0
  - six: 1.16.0
  - sniffio: 1.3.0
  - soupsieve: 2.3.2.post1
  - stack-data: 0.6.2
  - terminado: 0.17.1
  - threadpoolctl: 3.1.0
  - tinycss2: 1.2.1
  - tomli: 2.0.1
  - torch: 1.13.1
  - torch-geometric: 2.2.0
  - torch-scatter: 2.1.0
  - torch-sparse: 0.6.16
  - torchmetrics: 0.11.1
  - torchvision: 0.2.2
  - tornado: 6.2
  - tqdm: 4.64.1
  - traitlets: 5.9.0
  - typing-extensions: 4.4.0
  - urllib3: 1.26.14
  - wcwidth: 0.2.6
  - webencodings: 0.5.1
  - websocket-client: 1.5.1
  - wheel: 0.38.4
  - widgetsnbextension: 4.0.5
  - yarl: 1.8.2
  - zipp: 3.12.0
• System:
  - OS: Darwin
  - architecture:
  - 64bit
  -
  - processor: arm
  - python: 3.11.0
  - version: Darwin Kernel Version 22.1.0: Sun Oct 9 20:15:09 PDT 2022; root:xnu-8792.41.9~2/RELEASE_ARM64_T6000

More info

No response

[awaelchli]

Hi @ayushnoori
What does torch.distributed.is_available() return for you?

[ayushnoori]

Thanks for your help, @awaelchli!