Merge branch 'master' into uv-for-pytorch-tests

Author: Borda

docs/source-pytorch/common/hooks.rst

@@ -83,13 +83,30 @@ with the source of each hook indicated:
     trainer.fit()
     │
     ├── setup(stage="fit")
-    │   └── [Callbacks only]
-    │
-    ├── on_fit_start()
+    │   ├── [LightningDataModule]
     │   ├── [Callbacks]
     │   ├── [LightningModule]
+    │   ├── [LightningModule.configure_shared_model()]
+    │   ├── [LightningModule.configure_model()]
+    │   ├── Strategy.restore_checkpoint_before_setup
+    │   │   ├── [LightningModule.on_load_checkpoint()]
+    │   │   ├── [LightningModule.load_state_dict()]
+    │   │   ├── [LightningDataModule.load_state_dict()]
+    │   │   ├── [Callbacks.on_load_checkpoint()]
+    │   │   └── [Callbacks.load_state_dict()]
     │   └── [Strategy]
     │
+    ├── on_fit_start()
+    │   ├── [Callbacks]
+    │   └── [LightningModule]
+    │
+    ├── Strategy.restore_checkpoint_after_setup
+    │   ├── [LightningModule.on_load_checkpoint()]
+    │   ├── [LightningModule.load_state_dict()]
+    │   ├── [LightningDataModule.load_state_dict()]
+    │   ├── [Callbacks.on_load_checkpoint()]
+    │   └── [Callbacks.load_state_dict()]
+    │
     ├── on_sanity_check_start()
     │   ├── [Callbacks]
     │   ├── [LightningModule]

docs/source-pytorch/tuning/profiler_basic.rst

@@ -121,3 +121,22 @@ This can be measured with the :class:`~lightning.pytorch.callbacks.device_stats_
 
 CPU metrics will be tracked by default on the CPU accelerator. To enable it for other accelerators set ``DeviceStatsMonitor(cpu_stats=True)``. To disable logging
 CPU metrics, you can specify ``DeviceStatsMonitor(cpu_stats=False)``.
+
+.. warning::
+
+    **Do not wrap** ``Trainer.fit()``, ``Trainer.validate()``, or other Trainer methods inside a manual
+    ``torch.profiler.profile`` context manager. This will cause unexpected crashes and cryptic errors due to
+    incompatibility between PyTorch Profiler's context management and Lightning's internal training loop.
+    Instead, always use the ``profiler`` argument in the ``Trainer`` constructor or the
+    :class:`~lightning.pytorch.profilers.pytorch.PyTorchProfiler` profiler class if you want to customize the profiling.
+
+    Example:
+
+    .. code-block:: python
+
+        from lightning.pytorch import Trainer
+        from lightning.pytorch.profilers import PytorchProfiler
+
+        trainer = Trainer(profiler="pytorch")
+        # or
+        trainer = Trainer(profiler=PytorchProfiler(dirpath=".", filename="perf_logs"))