Merge branch 'master' into feat/device_name

GdoongMathew · web-flow · commit 10a95b42eedd · 2025-10-15T21:44:14.000+08:00
diff --git a/.github/workflows/_legacy-checkpoints.yml b/.github/workflows/_legacy-checkpoints.yml
@@ -58,7 +58,7 @@ jobs:
       - uses: actions/checkout@v5
 
       - name: Install uv and set Python version
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: "3.9"
           # TODO: Avoid activating environment like this
diff --git a/.github/workflows/ci-tests-fabric.yml b/.github/workflows/ci-tests-fabric.yml
@@ -71,7 +71,7 @@ jobs:
       - uses: actions/checkout@v5
 
       - name: Install uv and set Python version
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: ${{ matrix.config.python-version || '3.9' }}
           # TODO: Avoid activating environment like this
diff --git a/.github/workflows/ci-tests-pytorch.yml b/.github/workflows/ci-tests-pytorch.yml
@@ -76,7 +76,7 @@ jobs:
       - uses: actions/checkout@v5
 
       - name: Install uv and set Python version
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: ${{ matrix.config.python-version || '3.9' }}
           # TODO: Avoid activating environment like this
diff --git a/.github/workflows/code-checks.yml b/.github/workflows/code-checks.yml
@@ -31,7 +31,7 @@ jobs:
       - uses: actions/checkout@v5
 
       - name: Install uv and set Python version
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: "3.11"
           # TODO: Avoid activating environment like this
diff --git a/.github/workflows/docs-build.yml b/.github/workflows/docs-build.yml
@@ -73,7 +73,7 @@ jobs:
           lfs: ${{ matrix.pkg-name == 'pytorch' }}
 
       - name: Install uv and set Python version
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: "3.10"
           # TODO: Avoid activating environment like this
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -23,7 +23,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -70,7 +70,7 @@ repos:
       - id: sphinx-lint
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.2
+    rev: v0.13.3
     hooks:
       # try to fix what is possible
       - id: ruff
@@ -95,8 +95,8 @@ repos:
               README.md
           )$
 
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.1.0
+  - repo: https://github.com/JoC0de/pre-commit-prettier
+    rev: v3.6.2
     hooks:
       - id: prettier
         # https://prettier.io/docs/en/options.html#print-width
diff --git a/Makefile b/Makefile
@@ -7,7 +7,11 @@ export SPHINX_MOCK_REQUIREMENTS=1
 # install only Lightning Trainer packages
 export PACKAGE_NAME=pytorch
 
+
+# In Lightning Studio, the `lightning` package comes pre-installed.
+# Uninstall it first to ensure the editable install works correctly.
 setup:
+	uv pip uninstall lightning pytorch-lightning lightning-fabric || true
 	uv pip install -r requirements.txt \
 	    -r requirements/pytorch/base.txt \
 	    -r requirements/pytorch/test.txt \
diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@
 <br/>
 <br/>
 
-**The deep learning framework to pretrai and finetune AI models.**
+**The deep learning framework to pretrain and finetune AI models.**
 
 **Deploying models?** Check out [LitServe](https://github.com/Lightning-AI/litserve?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme), the PyTorch Lightning for inference engines
 
diff --git a/docs/source-fabric/conf.py b/docs/source-fabric/conf.py
@@ -272,6 +272,7 @@
 
 nitpick_ignore_regex = [
     ("py:class", "typing.Self"),
+    ("py:data", "typing.Union"),
     # these are not generated with docs API ref
     ("py:class", "lightning.fabric.utilities.types.Optimizable"),
     ("py:class", "lightning.fabric.utilities.types.Steppable"),
diff --git a/docs/source-pytorch/conf.py b/docs/source-pytorch/conf.py
@@ -374,6 +374,7 @@ def _load_py_module(name: str, location: str) -> ModuleType:
 
 nitpick_ignore = [
     ("py:class", "typing.Self"),
+    ("py:data", "typing.Union"),
     # missing in generated API
     ("py:exc", "MisconfigurationException"),
     # TODO: generated list of all existing ATM, need to be fixed
@@ -648,6 +649,7 @@ def package_list_from_file(file):
     "https://medium.com/pytorch-lightning/quick-contribution-guide-86d977171b3a",
     "https://deepgenerativemodels.github.io/assets/slides/cs236_lecture11.pdf",
     "https://developer.habana.ai", # returns 403 error but redirects to intel.com documentation
+    "https://www.supermicro.com", # returns 403 error
     "https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html",
     "https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/",  # noqa: E501
     "https://stackoverflow.com/questions/66640705/how-can-i-install-grpcio-on-an-apple-m1-silicon-laptop",
diff --git a/requirements/fabric/test.txt b/requirements/fabric/test.txt
@@ -1,9 +1,10 @@
-coverage ==7.10.6
+coverage ==7.10.7
 numpy >=1.21.0, <1.27.0
 pytest ==8.4.2
 pytest-cov ==6.3.0
 pytest-timeout ==2.4.0
-pytest-rerunfailures ==16.0.1
+pytest-rerunfailures ==16.0.1; python_version < "3.10"
+pytest-rerunfailures ==16.1; python_version >= "3.10"
 pytest-random-order ==1.2.0
 click ==8.1.8; python_version < "3.11"
 click ==8.3.0; python_version > "3.10"
diff --git a/requirements/pytorch/docs.txt b/requirements/pytorch/docs.txt
@@ -1,7 +1,7 @@
 -r ../docs.txt
 
 nbformat  # used for generate empty notebook
-ipython[notebook] <9.6.0
+ipython[notebook] <9.7.0
 setuptools<81.0  # workaround for `error in ipython setup command: use_2to3 is invalid.`
 
 onnxscript >= 0.2.2, < 0.5.0
diff --git a/requirements/pytorch/test.txt b/requirements/pytorch/test.txt
@@ -1,16 +1,17 @@
-coverage ==7.10.6
+coverage ==7.10.7
 pytest ==8.4.2
 pytest-cov ==6.3.0
 pytest-timeout ==2.4.0
-pytest-rerunfailures ==16.0.1
+pytest-rerunfailures ==16.0.1; python_version < "3.10"
+pytest-rerunfailures ==16.1; python_version >= "3.10"
 pytest-random-order ==1.2.0
 
 # needed in tests
 cloudpickle >=1.3, <3.2.0
 scikit-learn >0.22.1, <1.8.0
 numpy >1.20.0, <1.27.0
 onnx >1.12.0, <1.20.0
-onnxruntime >=1.12.0, <1.23.0
+onnxruntime >=1.12.0, <1.24.0
 onnxscript >= 0.1.0, < 0.5.0
 psutil <7.1.1 # for `DeviceStatsMonitor`
 pandas >2.0, <2.4.0  # needed in benchmarks
diff --git a/src/lightning/pytorch/utilities/model_helpers.py b/src/lightning/pytorch/utilities/model_helpers.py
@@ -19,7 +19,7 @@
 
 from lightning_utilities.core.imports import RequirementCache
 from torch import nn
-from typing_extensions import Concatenate, ParamSpec
+from typing_extensions import Concatenate, ParamSpec, override
 
 import lightning.pytorch as pl
 
@@ -104,26 +104,32 @@ def _check_mixed_imports(instance: object) -> None:
 _R_co = TypeVar("_R_co", covariant=True)  # return type of the decorated method
 
 
-class _restricted_classmethod_impl(Generic[_T, _R_co, _P]):
+class _restricted_classmethod_impl(classmethod, Generic[_T, _P, _R_co]):
     """Drop-in replacement for @classmethod, but raises an exception when the decorated method is called on an instance
     instead of a class type."""
 
+    method: Callable[Concatenate[type[_T], _P], _R_co]
+
     def __init__(self, method: Callable[Concatenate[type[_T], _P], _R_co]) -> None:
+        super().__init__(method)
         self.method = method
 
-    def __get__(self, instance: Optional[_T], cls: type[_T]) -> Callable[_P, _R_co]:
+    @override
+    def __get__(self, instance: _T, cls: Optional[type[_T]] = None) -> Callable[_P, _R_co]:  # type: ignore[override]
         # The wrapper ensures that the method can be inspected, but not called on an instance
         @functools.wraps(self.method)
         def wrapper(*args: Any, **kwargs: Any) -> _R_co:
             # Workaround for https://github.com/pytorch/pytorch/issues/67146
             is_scripting = any(os.path.join("torch", "jit") in frameinfo.filename for frameinfo in inspect.stack())
+            cls_type = cls if cls is not None else type(instance)
             if instance is not None and not is_scripting:
                 raise TypeError(
-                    f"The classmethod `{cls.__name__}.{self.method.__name__}` cannot be called on an instance."
+                    f"The classmethod `{cls_type.__name__}.{self.method.__name__}` cannot be called on an instance."
                     " Please call it on the class type and make sure the return value is used."
                 )
-            return self.method(cls, *args, **kwargs)
+            return self.method(cls_type, *args, **kwargs)
 
+        wrapper.__func__ = self.method
         return wrapper
 
 
diff --git a/tests/tests_pytorch/graveyard/test_precision.py b/tests/tests_pytorch/graveyard/test_precision.py
@@ -3,10 +3,11 @@
 
 def test_precision_plugin_renamed_imports():
     # base class
+    from lightning.pytorch.plugins.precision.precision_plugin import PrecisionPlugin as PrecisionPlugin0
+
     from lightning.pytorch.plugins import PrecisionPlugin as PrecisionPlugin2
     from lightning.pytorch.plugins.precision import PrecisionPlugin as PrecisionPlugin1
     from lightning.pytorch.plugins.precision.precision import Precision
-    from lightning.pytorch.plugins.precision.precision_plugin import PrecisionPlugin as PrecisionPlugin0
 
     assert issubclass(PrecisionPlugin0, Precision)
     assert issubclass(PrecisionPlugin1, Precision)
diff --git a/tests/tests_pytorch/utilities/test_model_helpers.py b/tests/tests_pytorch/utilities/test_model_helpers.py
@@ -42,11 +42,12 @@ def test_is_overridden():
     reason="This test is ONLY relevant for the UNIFIED package",
 )
 def test_mixed_imports_unified():
-    from lightning.pytorch.utilities.compile import _maybe_unwrap_optimized as new_unwrap
-    from lightning.pytorch.utilities.model_helpers import is_overridden as new_is_overridden
     from pytorch_lightning.callbacks import EarlyStopping as OldEarlyStopping
     from pytorch_lightning.demos.boring_classes import BoringModel as OldBoringModel
 
+    from lightning.pytorch.utilities.compile import _maybe_unwrap_optimized as new_unwrap
+    from lightning.pytorch.utilities.model_helpers import is_overridden as new_is_overridden
+
     model = OldBoringModel()
     with pytest.raises(TypeError, match=r"`pytorch_lightning` object \(BoringModel\) to a `lightning.pytorch`"):
         new_unwrap(model)
