Call LightningDataModule.load_state_dict hook while restoring checkpoint using LightningDataModule.load_from_checkpoint

src/pytorch_lightning/CHANGELOG.md

@@ -276,6 +276,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an attribute error when running the tuner together with the `StochasticWeightAveraging` callback ([#14836](https://github.com/Lightning-AI/lightning/pull/14836))
 
 
+- Called `LightningDataModule.load_state_dict` hook while restoring checkpoint using `LightningDataModule.load_from_checkpoint` ([#14883](https://github.com/Lightning-AI/lightning/pull/14883))
+
+
 - Fixed torchscript error with containers of LightningModules ([#14904](https://github.com/Lightning-AI/lightning/pull/14904))
 
 

src/pytorch_lightning/core/saving.py

@@ -226,6 +226,8 @@ def _load_state(
     obj.on_load_checkpoint(checkpoint)
 
     if isinstance(obj, pl.LightningDataModule):
+        if obj.__class__.__qualname__ in checkpoint:
+            obj.load_state_dict(checkpoint[obj.__class__.__qualname__])
         return obj
 
     # load the state_dict on the model automatically

tests/tests_pytorch/models/test_hooks.py

@@ -25,6 +25,27 @@
 from tests_pytorch.helpers.runif import RunIf
 
 
+class HookedDataModule(BoringDataModule):
+    def __init__(self, called):
+        super().__init__()
+
+        def call(hook, fn, *args, **kwargs):
+            out = fn(*args, **kwargs)
+            d = {"name": hook}
+            if args:
+                d["args"] = args
+            if kwargs:
+                d["kwargs"] = kwargs
+            called.append(d)
+            return out
+
+        for h in get_members(LightningDataModule):
+            attr = getattr(self, h)
+            partial_h = partial(call, h, attr)
+            update_wrapper(partial_h, attr)
+            setattr(self, h, partial_h)
+
+
 @pytest.mark.parametrize("max_steps", [1, 2, 3])
 def test_on_before_zero_grad_called(tmpdir, max_steps):
     class CurrentTestModel(BoringModel):
@@ -911,26 +932,6 @@ def predict_dataloader(self):
 def test_trainer_datamodule_hook_system(tmpdir):
     """Test the LightningDataModule hook system."""
 
-    class HookedDataModule(BoringDataModule):
-        def __init__(self, called):
-            super().__init__()
-
-            def call(hook, fn, *args, **kwargs):
-                out = fn(*args, **kwargs)
-                d = {"name": hook}
-                if args:
-                    d["args"] = args
-                if kwargs:
-                    d["kwargs"] = kwargs
-                called.append(d)
-                return out
-
-            for h in get_members(LightningDataModule):
-                attr = getattr(self, h)
-                partial_h = partial(call, h, attr)
-                update_wrapper(partial_h, attr)
-                setattr(self, h, partial_h)
-
     model = BoringModel()
     batches = 2
     trainer = Trainer(
@@ -991,3 +992,43 @@ def call(hook, fn, *args, **kwargs):
         dict(name="teardown", kwargs=dict(stage="predict")),
     ]
     assert called == expected
+
+
+def test_load_from_checkpoint_hook_calls(tmpdir):
+    class CustomHookedDataModule(HookedDataModule):
+        def state_dict(self):
+            return {"foo": "bar"}
+
+    lm_called, ldm_called = [], []
+    model = HookedModel(lm_called)
+    datamodule = CustomHookedDataModule(ldm_called)
+    trainer = Trainer()
+    trainer.strategy.connect(model)
+    trainer._data_connector.attach_data(model, datamodule=datamodule)
+    ckpt_path = str(tmpdir / "file.ckpt")
+    trainer.save_checkpoint(ckpt_path)
+
+    datamodule_state_dict_key = datamodule.__class__.__qualname__
+    saved_ckpt = {
+        "callbacks": ANY,
+        "epoch": 0,
+        "global_step": 0,
+        "lr_schedulers": ANY,
+        "optimizer_states": ANY,
+        "pytorch-lightning_version": __version__,
+        "state_dict": ANY,
+        "loops": ANY,
+        datamodule_state_dict_key: {"foo": "bar"},
+    }
+
+    assert lm_called == [dict(name="on_save_checkpoint", args=(saved_ckpt,))]
+    assert ldm_called == [dict(name="state_dict"), dict(name="on_save_checkpoint", args=(saved_ckpt,))]
+
+    lm_called, ldm_called = [], []
+    model = HookedModel.load_from_checkpoint(ckpt_path, called=lm_called)
+    datamodule = CustomHookedDataModule.load_from_checkpoint(ckpt_path, called=ldm_called)
+    assert lm_called == [dict(name="on_load_checkpoint", args=({**saved_ckpt, "hyper_parameters": ANY},))]
+    assert ldm_called == [
+        dict(name="on_load_checkpoint", args=({**saved_ckpt, "datamodule_hyper_parameters": ANY},)),
+        dict(name="load_state_dict", args=(saved_ckpt[datamodule_state_dict_key],)),
+    ]