Merge branch 'master' into dependabot-pip-requirements-pytest-cov-7.0.0

Author: deependujha

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 \

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")

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:

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