Validate the model input of trainer methods (#13892)

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>

src/pytorch_lightning/CHANGELOG.md

@@ -16,7 +16,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
--
+- The `Trainer.{fit,validate,test,predict,tune}` methods now raise a useful error message if the input is not a `LightningModule` ([#13892](https://github.com/Lightning-AI/lightning/pull/13892))
 
 
 -

src/pytorch_lightning/trainer/trainer.py

@@ -696,6 +696,8 @@ class Trainer(
 
             datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
         """
+        if not isinstance(model, pl.LightningModule):
+            raise TypeError(f"`Trainer.fit()` requires a `LightningModule`, got: {model.__class__.__qualname__}")
         self.strategy.model = model
         self._call_and_handle_interrupt(
             self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
@@ -776,6 +778,8 @@ class Trainer(
             :meth:`~pytorch_lightning.core.module.LightningModule.validation_epoch_end`, etc.
             The length of the list corresponds to the number of validation dataloaders used.
         """
+        if model is not None and not isinstance(model, pl.LightningModule):
+            raise TypeError(f"`Trainer.validate()` requires a `LightningModule`, got: {model.__class__.__qualname__}")
         self.strategy.model = model or self.lightning_module
         return self._call_and_handle_interrupt(self._validate_impl, model, dataloaders, ckpt_path, verbose, datamodule)
 
@@ -864,6 +868,8 @@ class Trainer(
             :meth:`~pytorch_lightning.core.module.LightningModule.test_epoch_end`, etc.
             The length of the list corresponds to the number of test dataloaders used.
         """
+        if model is not None and not isinstance(model, pl.LightningModule):
+            raise TypeError(f"`Trainer.test()` requires a `LightningModule`, got: {model.__class__.__qualname__}")
         self.strategy.model = model or self.lightning_module
         return self._call_and_handle_interrupt(self._test_impl, model, dataloaders, ckpt_path, verbose, datamodule)
 
@@ -951,6 +957,8 @@ class Trainer(
         Returns:
             Returns a list of dictionaries, one for each provided dataloader containing their respective predictions.
         """
+        if model is not None and not isinstance(model, pl.LightningModule):
+            raise TypeError(f"`Trainer.predict()` requires a `LightningModule`, got: {model.__class__.__qualname__}")
         self.strategy.model = model or self.lightning_module
         return self._call_and_handle_interrupt(
             self._predict_impl, model, dataloaders, datamodule, return_predictions, ckpt_path
@@ -1033,6 +1041,9 @@ class Trainer(
 
             lr_find_kwargs: Arguments for :func:`~pytorch_lightning.tuner.lr_finder.lr_find`
         """
+        if not isinstance(model, pl.LightningModule):
+            raise TypeError(f"`Trainer.tune()` requires a `LightningModule`, got: {model.__class__.__qualname__}")
+
         Trainer._log_api_event("tune")
 
         self.state.fn = TrainerFn.TUNING

tests/tests_pytorch/trainer/test_trainer.py

@@ -20,12 +20,14 @@ from argparse import Namespace
 from contextlib import nullcontext
 from copy import deepcopy
 from pathlib import Path
+from re import escape
 from unittest import mock
 from unittest.mock import ANY, call, patch
 
 import cloudpickle
 import pytest
 import torch
+import torch.nn as nn
 from torch.multiprocessing import ProcessRaisedException
 from torch.nn.parallel.distributed import DistributedDataParallel
 from torch.optim import SGD
@@ -71,6 +73,21 @@ else:
     torch_test_assert_close = torch.testing.assert_allclose
 
 
+def test_trainer_error_when_input_not_lightning_module():
+    """Test that a useful error gets raised when the Trainer methods receive something other than a
+    LightningModule."""
+    trainer = Trainer()
+
+    for method in ("fit", "validate", "test", "predict"):
+        with pytest.raises(TypeError, match=escape(f"`Trainer.{method}()` requires a `LightningModule`, got: Linear")):
+            run_method = getattr(trainer, method)
+            run_method(nn.Linear(2, 2))
+
+    trainer = Trainer(auto_lr_find=True, auto_scale_batch_size=True)
+    with pytest.raises(TypeError, match=escape("`Trainer.tune()` requires a `LightningModule`, got: Linear")):
+        trainer.tune(nn.Linear(2, 2))
+
+
 @pytest.mark.parametrize("url_ckpt", [True, False])
 def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt):
     """Tests use case where trainer saves the model, and user loads it from tags independently."""