Call configure_model() in LM.load_from_checkpoint() (#19036)

src/lightning/pytorch/CHANGELOG.md

@@ -29,6 +29,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - The `Trainer.fit()` loop no longer calls `LightningModule.train()` at the start; it now preserves the user's configuration of frozen layers ([#18951](https://github.com/Lightning-AI/lightning/pull/18951))
 
 
+- The `LightningModule.load_from_checkpoint()` function now calls `.configure_model()` on the model if it is overridden, to ensure all layers can be loaded from the checkpoint ([#19036](https://github.com/Lightning-AI/lightning/pull/19036))
+
+
 ### Deprecated
 
 - Deprecated all precision plugin classes under `lightning.pytorch.plugins` with the suffix `Plugin` in the name ([#18840](https://github.com/Lightning-AI/lightning/pull/18840))

src/lightning/pytorch/core/hooks.py

@@ -336,7 +336,8 @@ def configure_model(self) -> None:
         :meth:`~lightning.pytorch.trainer.trainer.Trainer.init_module` context manager.
 
         This hook is called during each of fit/val/test/predict stages in the same process, so ensure that
-        implementation of this hook is idempotent.
+        implementation of this hook is **idempotent**, i.e., after the first time the hook is called, subsequent calls
+        to it should be a no-op.
 
         """
 

src/lightning/pytorch/core/module.py

@@ -1519,6 +1519,13 @@ def load_from_checkpoint(
             **class** to call it instead of the :class:`LightningModule` instance, or a
             ``TypeError`` will be raised.
 
+        Note:
+            To ensure all layers can be loaded from the checkpoint, this function will call
+            :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_model` directly after instantiating the
+            model if this hook is overridden in your LightningModule. However, note that ``load_from_checkpoint`` does
+            not support loading sharded checkpoints, and you may run out of memory if the model is too large. In this
+            case, consider loading through the Trainer via ``.fit(ckpt_path=...)``.
+
         Example::
 
             # load weights without mapping ...

src/lightning/pytorch/core/saving.py

@@ -38,6 +38,7 @@
 from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
 from lightning.pytorch.utilities.migration import pl_legacy_patch
 from lightning.pytorch.utilities.migration.utils import _pl_migrate_checkpoint
+from lightning.pytorch.utilities.model_helpers import is_overridden
 from lightning.pytorch.utilities.parsing import parse_class_init_keys
 from lightning.pytorch.utilities.rank_zero import rank_zero_warn
 
@@ -157,6 +158,9 @@ def _load_state(
     obj = cls(**_cls_kwargs)
 
     if isinstance(obj, pl.LightningModule):
+        if is_overridden("configure_model", obj):
+            obj.configure_model()
+
         # give model a chance to load something
         obj.on_load_checkpoint(checkpoint)
 

tests/tests_pytorch/models/test_hooks.py

@@ -877,13 +877,22 @@ def test_trainer_datamodule_hook_system(tmpdir):
     assert called == expected
 
 
-def test_load_from_checkpoint_hook_calls(tmpdir):
+@pytest.mark.parametrize("override_configure_model", [True, False])
+def test_load_from_checkpoint_hook_calls(override_configure_model, tmpdir):
     class CustomHookedDataModule(HookedDataModule):
         def state_dict(self):
             return {"foo": "bar"}
 
+    class CustomHookedModel(HookedModel):
+        pass
+
+    if not override_configure_model:
+        CustomHookedModel.configure_model = None
+
     lm_called, ldm_called = [], []
-    model = HookedModel(lm_called)
+    model = CustomHookedModel(lm_called)
+    assert is_overridden("configure_model", model) == override_configure_model
+
     datamodule = CustomHookedDataModule(ldm_called)
     trainer = Trainer()
     trainer.strategy.connect(model)
@@ -908,9 +917,12 @@ def state_dict(self):
     assert ldm_called == [{"name": "state_dict"}]
 
     lm_called, ldm_called = [], []
-    _ = HookedModel.load_from_checkpoint(ckpt_path, called=lm_called)
+    _ = CustomHookedModel.load_from_checkpoint(ckpt_path, called=lm_called)
     _ = CustomHookedDataModule.load_from_checkpoint(ckpt_path, called=ldm_called)
-    assert lm_called == [{"name": "on_load_checkpoint", "args": ({**saved_ckpt, "hyper_parameters": ANY},)}]
+
+    expected_lm_called = [{"name": "configure_model"}] if override_configure_model else []
+    expected_lm_called += [{"name": "on_load_checkpoint", "args": ({**saved_ckpt, "hyper_parameters": ANY},)}]
+    assert lm_called == expected_lm_called
     assert ldm_called == [{"name": "load_state_dict", "args": (saved_ckpt[datamodule_state_dict_key],)}]