Repository: Lightning-AI/pytorch-lightning
Issue category: Feature Request

Issue:
Support restoring callbacks' status when predicting
### Description & Motivation

I implemented EMA using Callback. However, when predicting, the callback's weight isn't loaded back 

### Pitch

remove the condition at: https://github.com/Lightning-AI/pytorch-lightning/blob/f5d82504da252a255df268bd2bb99bf3f2886d27/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py#L400

### Alternatives

_No response_

### Additional context

_No response_

cc @borda


Hints:

Patch:
diff --git a/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py b/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py
index c73ceb32ec77f..a41f87d418ebe 100644
--- a/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py
+++ b/src/lightning/pytorch/trainer/connectors/checkpoint_connector.py
@@ -397,9 +397,7 @@ def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None
         self.resume_start(checkpoint_path)
         self.restore_model()
         self.restore_datamodule()
-        if self.trainer.state.fn == TrainerFn.FITTING:
-            # restore callback states
-            self.restore_callbacks()
+        self.restore_callbacks()
 
     def dump_checkpoint(self, weights_only: bool = False) -> dict:
         """Creating a model checkpoint dictionary object from various component states.


Test patch:
diff --git a/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py b/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py
index 39911f9eddc7f..d29e2285e983c 100644
--- a/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py
+++ b/tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py
@@ -18,7 +18,7 @@
 import pytest
 import torch
 from lightning.pytorch import Trainer
-from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks import Callback, ModelCheckpoint
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.trainer.states import TrainerFn
 from lightning.pytorch.utilities.migration.utils import _set_version
@@ -234,3 +234,53 @@ def test_strict_loading(strict_loading, expected, tmp_path):
     trainer = Trainer(default_root_dir=tmp_path, barebones=True, max_steps=2)
     trainer.fit(model, ckpt_path=(tmp_path / "checkpoint.ckpt"))
     model.load_state_dict.assert_called_once_with(ANY, strict=expected)
+
+
+@pytest.mark.parametrize("trainer_fn", ["validate", "test", "predict"])
+def test_restore_callbacks_in_non_fit_phases(tmp_path, trainer_fn):
+    """Test that callbacks are properly restored in non-fit phases."""
+
+    class TestCallback(Callback):
+        def __init__(self):
+            self.restored = False
+
+        def on_load_checkpoint(self, trainer, pl_module, checkpoint):
+            if "callbacks" in checkpoint:
+                callback_state = checkpoint["callbacks"][self.__class__.__name__]
+                self.restored = callback_state["restored"]
+
+        def state_dict(self):
+            return {"restored": self.restored}
+
+        def on_save_checkpoint(self, trainer, pl_module, checkpoint):
+            checkpoint["callbacks"] = checkpoint.get("callbacks", {})
+            checkpoint["callbacks"][self.__class__.__name__] = self.state_dict()
+
+    # First create and train a model with the callback
+    callback = TestCallback()
+    model = BoringModel()
+    trainer = Trainer(default_root_dir=tmp_path, callbacks=[callback], max_steps=1)
+    trainer.fit(model)
+
+    # Set the callback state to True before saving
+    callback.restored = True
+    ckpt_path = tmp_path / "checkpoint.ckpt"
+    trainer.save_checkpoint(ckpt_path)
+
+    # Now create new instances and test restoration
+    new_callback = TestCallback()
+    new_model = BoringModel()
+    assert not new_callback.restored  # Should start False
+
+    new_trainer = Trainer(default_root_dir=tmp_path, callbacks=[new_callback])
+
+    # Connect the model and restore callbacks before evaluation
+    new_trainer.strategy.connect(new_model)
+    new_trainer._checkpoint_connector.resume_start(ckpt_path)
+    new_trainer._checkpoint_connector.restore_callbacks()
+
+    # Run the evaluation phase (validate/test/predict)
+    fn = getattr(new_trainer, trainer_fn)
+    fn(new_model, ckpt_path=ckpt_path)
+
+    assert new_callback.restored  # Should be True after loading the checkpoint