[CLI] Support custom trainers without callbacks (#13138)

Author: carmocca

src/pytorch_lightning/CHANGELOG.md

@@ -73,6 +73,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `teardown()` method to `Accelerator` ([#11935](https://github.com/PyTorchLightning/pytorch-lightning/pull/11935))
 
 
+- Added support for using custom Trainers that don't include callbacks using the CLI ([#13138](https://github.com/PyTorchLightning/pytorch-lightning/pull/13138))
+
+
 - Added a `timeout` argument to `DDPStrategy`. ([#13244](https://github.com/PyTorchLightning/pytorch-lightning/pull/13244))
 
 

src/pytorch_lightning/utilities/cli.py

@@ -602,30 +602,36 @@ def instantiate_trainer(self, **kwargs: Any) -> Trainer:
             kwargs: Any custom trainer arguments.
         """
         extra_callbacks = [self._get(self.config_init, c) for c in self._parser(self.subcommand).callback_keys]
-        trainer_config = {**self._get(self.config_init, "trainer"), **kwargs}
+        trainer_config = {**self._get(self.config_init, "trainer", default={}), **kwargs}
         return self._instantiate_trainer(trainer_config, extra_callbacks)
 
     def _instantiate_trainer(self, config: Dict[str, Any], callbacks: List[Callback]) -> Trainer:
-        if config["callbacks"] is None:
-            config["callbacks"] = []
-        elif not isinstance(config["callbacks"], list):
-            config["callbacks"] = [config["callbacks"]]
-        assert isinstance(config["callbacks"], list)  # to handle mypy false positive
-        config["callbacks"].extend(callbacks)
-        if "callbacks" in self.trainer_defaults:
-            if isinstance(self.trainer_defaults["callbacks"], list):
-                config["callbacks"].extend(self.trainer_defaults["callbacks"])
-            else:
-                config["callbacks"].append(self.trainer_defaults["callbacks"])
-        if self.save_config_callback and not config["fast_dev_run"]:
-            config_callback = self.save_config_callback(
-                self._parser(self.subcommand),
-                self.config.get(str(self.subcommand), self.config),
-                self.save_config_filename,
-                overwrite=self.save_config_overwrite,
-                multifile=self.save_config_multifile,
+        key = "callbacks"
+        if key in config:
+            if config[key] is None:
+                config[key] = []
+            elif not isinstance(config[key], list):
+                config[key] = [config[key]]
+            config[key].extend(callbacks)
+            if key in self.trainer_defaults:
+                if isinstance(self.trainer_defaults[key], list):
+                    config[key].extend(self.trainer_defaults[key])
+                else:
+                    config[key].append(self.trainer_defaults[key])
+            if self.save_config_callback and not config.get("fast_dev_run", False):
+                config_callback = self.save_config_callback(
+                    self._parser(self.subcommand),
+                    self.config.get(str(self.subcommand), self.config),
+                    self.save_config_filename,
+                    overwrite=self.save_config_overwrite,
+                    multifile=self.save_config_multifile,
+                )
+                config[key].append(config_callback)
+        else:
+            rank_zero_warn(
+                f"The `{self.trainer_class.__qualname__}` class does not expose the `{key}` argument so they will"
+                " not be included."
             )
-            config["callbacks"].append(config_callback)
         return self.trainer_class(**config)
 
     def _parser(self, subcommand: Optional[str]) -> LightningArgumentParser:

tests/tests_pytorch/utilities/test_cli.py

@@ -1483,6 +1483,22 @@ def test_cli_auto_seeding():
     assert cli.config["seed_everything"] == 123  # the original seed is kept
 
 
+def test_cli_trainer_no_callbacks():
+    class MyTrainer(Trainer):
+        def __init__(self):
+            super().__init__()
+
+    class MyCallback(Callback):
+        ...
+
+    match = "MyTrainer` class does not expose the `callbacks"
+    with mock.patch("sys.argv", ["any.py"]), pytest.warns(UserWarning, match=match):
+        cli = LightningCLI(
+            BoringModel, run=False, trainer_class=MyTrainer, trainer_defaults={"callbacks": MyCallback()}
+        )
+    assert not any(isinstance(cb, MyCallback) for cb in cli.trainer.callbacks)
+
+
 def test_unresolvable_import_paths():
     class TestModel(BoringModel):
         def __init__(self, a_func: Callable = torch.softmax):