Merge branch 'master' into fix/skipif-deepspeed

deependujha · web-flow · commit 41f9d3a66ca4 · 2025-10-17T11:36:38.000+05:30
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
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/docs/source-pytorch/deploy/production_advanced_2.rst b/docs/source-pytorch/deploy/production_advanced_2.rst
@@ -7,15 +7,20 @@ Deploy models into production (advanced)
 
 ----
 
-*********************************
-Compile your model to TorchScript
-*********************************
-`TorchScript <https://pytorch.org/docs/stable/jit.html>`_ allows you to serialize your models in a way that it can be loaded in non-Python environments.
-The ``LightningModule`` has a handy method :meth:`~lightning.pytorch.core.LightningModule.to_torchscript` that returns a scripted module which you
-can save or directly use.
+************************************
+Export your model with torch.export
+************************************
+
+`torch.export <https://pytorch.org/docs/stable/export.html>`_ is the recommended way to capture PyTorch models for
+deployment in production environments. It produces a clean intermediate representation with strong soundness guarantees,
+making models suitable for inference optimization and cross-platform deployment.
+You can export any ``LightningModule`` using the ``torch.export.export()`` API.
 
 .. testcode:: python
 
+    import torch
+    from torch.export import export
+
     class SimpleModel(LightningModule):
         def __init__(self):
             super().__init__()
@@ -25,25 +30,27 @@ can save or directly use.
             return torch.relu(self.l1(x.view(x.size(0), -1)))
 
 
-    # create the model
+    # create the model and example input
     model = SimpleModel()
-    script = model.to_torchscript()
+    example_input = torch.randn(1, 64)
 
-    # save for use in production environment
-    torch.jit.save(script, "model.pt")
+    # export the model
+    exported_program = export(model, (example_input,))
 
-It is recommended that you install the latest supported version of PyTorch to use this feature without limitations.
+    # save for use in production environment
+    torch.export.save(exported_program, "model.pt2")
 
-Once you have the exported model, you can run it in PyTorch or C++ runtime:
+It is recommended that you install the latest supported version of PyTorch to use this feature without
+limitations. Once you have the exported model, you can load and run it:
 
 .. code-block:: python
 
     inp = torch.rand(1, 64)
-    scripted_module = torch.jit.load("model.pt")
-    output = scripted_module(inp)
+    loaded_program = torch.export.load("model.pt2")
+    output = loaded_program.module()(inp)
 
 
-If you want to script a different method, you can decorate the method with :func:`torch.jit.export`:
+For more complex models, you can also export specific methods by creating a wrapper:
 
 .. code-block:: python
 
@@ -54,7 +61,6 @@ If you want to script a different method, you can decorate the method with :func
             self.dropout = nn.Dropout()
             self.mc_iteration = mc_iteration
 
-        @torch.jit.export
         def predict_step(self, batch, batch_idx):
             # enable Monte Carlo Dropout
             self.dropout.train()
@@ -66,4 +72,11 @@ If you want to script a different method, you can decorate the method with :func
 
 
     model = LitMCdropoutModel(...)
-    script = model.to_torchscript(file_path="model.pt", method="script")
+    example_batch = torch.randn(32, 10)  # example input
+
+    # Export the predict_step method
+    exported_program = torch.export.export(
+        lambda batch, idx: model.predict_step(batch, idx),
+        (example_batch, 0)
+    )
+    torch.export.save(exported_program, "mc_dropout_model.pt2")
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/CHANGELOG.md b/src/lightning/pytorch/CHANGELOG.md
@@ -52,6 +52,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed preventing recursive symlink creation iwhen `save_last='link'` and `save_top_k=-1` ([#21186](https://github.com/Lightning-AI/pytorch-lightning/pull/21186))
 
 
+- Fixed `ModelPruning` sparsity logging bug that caused incorrect sparsity percentages ([#21223](https://github.com/Lightning-AI/pytorch-lightning/pull/21223))
+
+
 - Fixed `LightningCLI` loading of hyperparameters from `ckpt_path` failing for subclass model mode ([#21246](https://github.com/Lightning-AI/pytorch-lightning/pull/21246))
 
 
diff --git a/src/lightning/pytorch/callbacks/pruning.py b/src/lightning/pytorch/callbacks/pruning.py
@@ -349,7 +349,7 @@ def apply_pruning(self, amount: Union[int, float]) -> None:
     def _log_sparsity_stats(
         self, prev: list[tuple[int, int]], curr: list[tuple[int, int]], amount: Union[int, float] = 0
     ) -> None:
-        total_params = sum(p.numel() for layer, _ in self._parameters_to_prune for p in layer.parameters())
+        total_params = sum(total for _, total in curr)
         prev_total_zeros = sum(zeros for zeros, _ in prev)
         curr_total_zeros = sum(zeros for zeros, _ in curr)
         log.info(
diff --git a/src/lightning/pytorch/trainer/connectors/logger_connector/result.py b/src/lightning/pytorch/trainer/connectors/logger_connector/result.py
@@ -91,8 +91,7 @@ def _generate_sync_fn(self) -> None:
         """Used to compute the syncing function and cache it."""
         fn = self.no_op if self.fn is None or not self.should or self.rank_zero_only else self.fn
         # save the function as `_fn` as the meta are being re-created and the object references need to match.
-        # ignore typing, bad support for `partial`: mypy/issues/1484
-        self._fn: Callable = partial(fn, reduce_op=self.op, group=self.group)  # type: ignore[unused-ignore]
+        self._fn: Callable = partial(fn, reduce_op=self.op, group=self.group)
 
     @property
     def __call__(self) -> Any:
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/callbacks/test_pruning.py b/tests/tests_pytorch/callbacks/test_pruning.py
diff --git a/tests/tests_pytorch/graveyard/test_precision.py b/tests/tests_pytorch/graveyard/test_precision.py
diff --git a/tests/tests_pytorch/utilities/test_model_helpers.py b/tests/tests_pytorch/utilities/test_model_helpers.py
