precommit: drop Black in favor of Ruff (#19380)

.pre-commit-config.yaml

@@ -84,25 +84,11 @@ repos:
           - flake8-return
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.9"
+    rev: "v0.1.15"
     hooks:
       - id: ruff
         args: ["--fix", "--preview"]
 
-  - repo: https://github.com/psf/black
-    rev: 23.12.1
-    hooks:
-      - id: black
-        name: Format code
-        exclude: docs/source-app
-
-  - repo: https://github.com/asottile/blacken-docs
-    rev: 1.16.0
-    hooks:
-      - id: blacken-docs
-        args: ["--line-length=120"]
-        exclude: docs/source-app
-
   - repo: https://github.com/executablebooks/mdformat
     rev: 0.7.17
     hooks:

docs/source-pytorch/extensions/strategy.rst

@@ -77,7 +77,7 @@ The below table lists all relevant strategies available in Lightning with their
      - Strategy for multi-process single-device training on one or multiple nodes. :ref:`Learn more. <accelerators/gpu_intermediate:Distributed Data Parallel>`
    * - ddp_spawn
      - :class:`~lightning.pytorch.strategies.DDPStrategy`
-     - Same as "ddp" but launches processes using :func:`torch.multiprocessing.spawn` method and joins processes after training finishes. :ref:`Learn more. <accelerators/gpu_intermediate:Distributed Data Parallel Spawn>`
+     - Same as "ddp" but launches processes using ``torch.multiprocessing.spawn`` method and joins processes after training finishes. :ref:`Learn more. <accelerators/gpu_intermediate:Distributed Data Parallel Spawn>`
    * - deepspeed
      - :class:`~lightning.pytorch.strategies.DeepSpeedStrategy`
      - Provides capabilities to run training using the DeepSpeed library, with training optimizations for large billion parameter models. :doc:`Learn more. <../advanced/model_parallel/deepspeed>`

pyproject.toml

@@ -93,6 +93,8 @@ ignore-init-module-imports = true
     "S113",  # todo: Probable use of requests call without timeout
     "S301",  # todo: `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue
     "S324",  # todo: Probable use of insecure hash functions in `hashlib`
+    "S403",  # todo: `pickle`, `cPickle`, `dill`, and `shelve` modules are possibly insecure
+    "S404",  # todo: `subprocess` module is possibly insecure
     "S602",  # todo: `subprocess` call with `shell=True` identified, security issue
     "S603",  # todo: `subprocess` call: check for execution of untrusted input
     "S605",  # todo: Starting a process with a shell: seems safe, but may be changed in the future; consider rewriting without `shell`
@@ -108,6 +110,8 @@ ignore-init-module-imports = true
     "S311",  # todo: Standard pseudo-random generators are not suitable for cryptographic purposes
     "S108",  # todo: Probable insecure usage of temporary file or directory: "/tmp/sys-customizations-sync"
     "S202",  # Uses of `tarfile.extractall()`
+    "S403",  # `pickle`, `cPickle`, `dill`, and `shelve` modules are possibly insecure
+    "S404",  # `subprocess` module is possibly insecure
     "S602",  # todo: `subprocess` call with `shell=True` identified, security issue
     "S603",  # todo: `subprocess` call: check for execution of untrusted input
     "S605",  # todo: Starting a process with a shell: seems safe, but may be changed in the future; consider rewriting without `shell`

tests/tests_pytorch/utilities/test_compile.py

@@ -16,7 +16,6 @@ from unittest import mock
 
 import pytest
 import torch
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
 from lightning.pytorch import LightningModule, Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.utilities.compile import from_compiled, to_uncompiled
@@ -26,7 +25,8 @@ from tests_pytorch.conftest import mock_cuda_count
 from tests_pytorch.helpers.runif import RunIf
 
 
-@pytest.mark.skipif(sys.platform == "darwin" and not _TORCH_GREATER_EQUAL_2_1, reason="Fix for MacOS in PyTorch 2.1")
+# https://github.com/pytorch/pytorch/issues/95708
+@pytest.mark.skipif(sys.platform == "darwin", reason="fatal error: 'omp.h' file not found")
 @RunIf(dynamo=True)
 @mock.patch("lightning.pytorch.trainer.call._call_and_handle_interrupt")
 def test_trainer_compiled_model(_, tmp_path, monkeypatch, mps_count_0):
@@ -112,7 +112,8 @@ def test_compile_uncompile():
     assert not has_dynamo(to_uncompiled_model.predict_step)
 
 
-@pytest.mark.skipif(sys.platform == "darwin" and not _TORCH_GREATER_EQUAL_2_1, reason="Fix for MacOS in PyTorch 2.1")
+# https://github.com/pytorch/pytorch/issues/95708
+@pytest.mark.skipif(sys.platform == "darwin", reason="fatal error: 'omp.h' file not found")
 @RunIf(dynamo=True)
 def test_trainer_compiled_model_that_logs(tmp_path):
     class MyModel(BoringModel):
@@ -137,7 +138,8 @@ def test_trainer_compiled_model_that_logs(tmp_path):
     assert set(trainer.callback_metrics) == {"loss"}
 
 
-@pytest.mark.skipif(sys.platform == "darwin" and not _TORCH_GREATER_EQUAL_2_1, reason="Fix for MacOS in PyTorch 2.1")
+# https://github.com/pytorch/pytorch/issues/95708
+@pytest.mark.skipif(sys.platform == "darwin", reason="fatal error: 'omp.h' file not found")
 @RunIf(dynamo=True)
 def test_trainer_compiled_model_test(tmp_path):
     model = BoringModel()