Fix setup hook order [wip]

pytorch_lightning/trainer/trainer.py

@@ -461,6 +461,7 @@ def fit(
         # ----------------------------
         # SET UP TRAINING
         # ----------------------------
+        self.call_setup_hook(model)
         self.call_hook("on_before_accelerator_backend_setup", model)
         self.accelerator_backend.setup(self, model)
         self.setup_trainer(model)
@@ -476,8 +477,6 @@ def fit(
         self.training_type_plugin.pre_training()
         self.precision_plugin.pre_training()
 
-        self.call_setup_hook(self.lightning_module)
-
         # double dispatch: let the plugin initiate the training/test loop.
         if self.testing:
             self.training_type_plugin.start_testing(self)

tests/callbacks/test_callbacks.py

@@ -53,9 +53,9 @@ def test_trainer_callback_system(torch_save):
     assert callback_mock.method_calls == [
         call.on_init_start(trainer),
         call.on_init_end(trainer),
+        call.setup(trainer, model, 'fit'),
         call.on_before_accelerator_backend_setup(trainer, model),
         call.on_fit_start(trainer, model),
-        call.setup(trainer, model, 'fit'),
         call.on_pretrain_routine_start(trainer, model),
         call.on_pretrain_routine_end(trainer, model),
         call.on_sanity_check_start(trainer, model),
@@ -108,9 +108,9 @@ def test_trainer_callback_system(torch_save):
     assert callback_mock.method_calls == [
         call.on_init_start(trainer),
         call.on_init_end(trainer),
+        call.setup(trainer, model, 'test'),
         call.on_before_accelerator_backend_setup(trainer, model),
         call.on_fit_start(trainer, model),
-        call.setup(trainer, model, 'test'),
         call.on_test_start(trainer, model),
         call.on_test_epoch_start(trainer, model),
         call.on_test_batch_start(trainer, model, ANY, 0, 0),

tests/callbacks/test_finetuning_callback.py

@@ -19,6 +19,7 @@
 
 from pytorch_lightning import LightningModule, seed_everything, Trainer
 from pytorch_lightning.callbacks import BackboneFinetuning, BaseFinetuning
+from pytorch_lightning.callbacks.base import Callback
 from tests.helpers import BoringModel, RandomDataset
 
 
@@ -215,3 +216,31 @@ def __init__(self):
             assert torch.equal(optimizer.param_groups[2]["params"][0], model.backbone[2].weight)
             assert torch.equal(optimizer.param_groups[2]["params"][1], model.backbone[3].weight)
             assert torch.equal(optimizer.param_groups[2]["params"][2], model.backbone[4].weight)
+
+
+def test_on_before_accelerator_backend_setup(tmpdir):
+    """
+    `on_before_accelerator_backend_setup` hook is used by finetuning callbacks to freeze the model before
+    before configure_optimizers function call.
+    """
+
+    class TestCallback(Callback):
+
+        def on_before_accelerator_backend_setup(self, trainer, pl_module):
+            pl_module.on_before_accelerator_backend_setup_called = True
+
+    class TestModel(BoringModel):
+
+        def __init__(self):
+            super().__init__()
+            self.on_before_accelerator_backend_setup_called = False
+
+        def configure_optimizers(self):
+            assert self.on_before_accelerator_backend_setup_called
+            return super().configure_optimizers()
+
+    model = TestModel()
+    callback = TestCallback()
+
+    trainer = Trainer(default_root_dir=tmpdir, callbacks=[callback], fast_dev_run=True)
+    trainer.fit(model)

tests/trainer/test_trainer.py

@@ -1742,6 +1742,9 @@ def training_epoch_end(self, *args, **kwargs):
 
 
 def test_trainer_access_in_configure_optimizers(tmpdir):
+    """
+    Verify that the configure optimizer function can reference the trainer.
+    """
 
     class TestModel(BoringModel):
 
@@ -1753,3 +1756,30 @@ def configure_optimizers(self):
     model = TestModel()
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
     trainer.fit(model, train_data)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+def test_setup_hook_move_to_device_correctly(tmpdir):
+    """
+    Verify that if a user defines a layer in the setup hook function, this is moved to the correct device.
+    """
+
+    class TestModel(BoringModel):
+
+        def setup(self, stage: str) -> None:
+            self.new_layer = torch.nn.Linear(2, 2)
+
+        def training_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            # will crash if not moved to correct device
+            output = self.new_layer(output)
+            loss = self.loss(batch, output)
+            return {"loss": loss}
+
+    # fake data
+    train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
+
+    # model
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=1)
+    trainer.fit(model, train_data)