Call configure_module before freeze_before_training

src/lightning/pytorch/trainer/trainer.py

@@ -941,9 +941,9 @@ def _run(
         log.debug(f"{self.__class__.__name__}: preparing data")
         self._data_connector.prepare_data()
 
-        call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
         log.debug(f"{self.__class__.__name__}: configuring model")
         call._call_configure_model(self)
+        call._call_setup_hook(self)  # allow user to set up LightningModule in accelerator environment
 
         # check if we should delay restoring checkpoint till later
         if not self.strategy.restore_checkpoint_after_setup:

tests/tests_pytorch/callbacks/test_finetuning_callback.py

@@ -431,3 +431,51 @@ def test_unsupported_strategies(tmp_path):
     trainer = Trainer(accelerator="cpu", strategy="deepspeed", callbacks=[callback])
     with pytest.raises(NotImplementedError, match="does not support running with the DeepSpeed strategy"):
         callback.setup(trainer, model, stage=None)
+
+
+def test_finetuning_with_configure_model(tmp_path):
+    """Test that BaseFinetuning works correctly with configure_model by ensuring freeze_before_training is called after
+    configure_model but before training starts."""
+
+    class TrackingFinetuningCallback(BaseFinetuning):
+        def __init__(self):
+            super().__init__()
+
+        def freeze_before_training(self, pl_module):
+            assert hasattr(pl_module, "backbone"), "backbone should be configured before freezing"
+            self.freeze(pl_module.backbone)
+
+        def finetune_function(self, pl_module, epoch, optimizer):
+            pass
+
+    class TestModel(LightningModule):
+        def __init__(self):
+            super().__init__()
+            self.configure_model_called_count = 0
+
+        def configure_model(self):
+            self.backbone = nn.Linear(32, 32)
+            self.classifier = nn.Linear(32, 2)
+            self.configure_model_called_count += 1
+
+        def forward(self, x):
+            x = self.backbone(x)
+            return self.classifier(x)
+
+        def training_step(self, batch, batch_idx):
+            return self.forward(batch).sum()
+
+        def configure_optimizers(self):
+            return torch.optim.SGD(self.parameters(), lr=0.1)
+
+    model = TestModel()
+    callback = TrackingFinetuningCallback()
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        callbacks=[callback],
+        max_epochs=1,
+        limit_train_batches=1,
+    )
+
+    trainer.fit(model, torch.randn(10, 32))
+    assert model.configure_model_called_count == 1

tests/tests_pytorch/models/test_hooks.py

@@ -472,11 +472,11 @@ def training_step(self, batch, batch_idx):
     expected = [
         {"name": "configure_callbacks"},
         {"name": "prepare_data"},
+        {"name": "configure_model"},
         {"name": "Callback.setup", "args": (trainer, model), "kwargs": {"stage": "fit"}},
         {"name": "setup", "kwargs": {"stage": "fit"}},
         # DeepSpeed needs the batch size to figure out throughput logging
         *([{"name": "train_dataloader"}] if using_deepspeed else []),
-        {"name": "configure_model"},
         {"name": "configure_optimizers"},
         {"name": "Callback.on_fit_start", "args": (trainer, model)},
         {"name": "on_fit_start"},
@@ -571,9 +571,9 @@ def test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs(tmp_path):
     expected = [
         {"name": "configure_callbacks"},
         {"name": "prepare_data"},
+        {"name": "configure_model"},
         {"name": "Callback.setup", "args": (trainer, model), "kwargs": {"stage": "fit"}},
         {"name": "setup", "kwargs": {"stage": "fit"}},
-        {"name": "configure_model"},
         {"name": "on_load_checkpoint", "args": (loaded_ckpt,)},
         {"name": "Callback.on_load_checkpoint", "args": (trainer, model, loaded_ckpt)},
         {"name": "Callback.load_state_dict", "args": ({"foo": True},)},
@@ -651,9 +651,9 @@ def test_trainer_model_hook_system_fit_no_val_and_resume_max_steps(tmp_path):
     expected = [
         {"name": "configure_callbacks"},
         {"name": "prepare_data"},
+        {"name": "configure_model"},
         {"name": "Callback.setup", "args": (trainer, model), "kwargs": {"stage": "fit"}},
         {"name": "setup", "kwargs": {"stage": "fit"}},
-        {"name": "configure_model"},
         {"name": "on_load_checkpoint", "args": (loaded_ckpt,)},
         {"name": "Callback.on_load_checkpoint", "args": (trainer, model, loaded_ckpt)},
         {"name": "Callback.load_state_dict", "args": ({"foo": True},)},
@@ -719,9 +719,9 @@ def test_trainer_model_hook_system_eval(tmp_path, override_on_x_model_train, bat
     expected = [
         {"name": "configure_callbacks"},
         {"name": "prepare_data"},
+        {"name": "configure_model"},
         {"name": "Callback.setup", "args": (trainer, model), "kwargs": {"stage": verb}},
         {"name": "setup", "kwargs": {"stage": verb}},
-        {"name": "configure_model"},
         {"name": "zero_grad"},
         *(hooks if batches else []),
         {"name": "Callback.teardown", "args": (trainer, model), "kwargs": {"stage": verb}},
@@ -746,9 +746,9 @@ def test_trainer_model_hook_system_predict(tmp_path):
     expected = [
         {"name": "configure_callbacks"},
         {"name": "prepare_data"},
+        {"name": "configure_model"},
         {"name": "Callback.setup", "args": (trainer, model), "kwargs": {"stage": "predict"}},
         {"name": "setup", "kwargs": {"stage": "predict"}},
-        {"name": "configure_model"},
         {"name": "zero_grad"},
         {"name": "predict_dataloader"},
         {"name": "train", "args": (False,)},