[CLI] Shorthand notation to instantiate models

docs/source/common/lightning_cli.rst

@@ -415,22 +415,59 @@ as described above:
 
     $  python ... --trainer.callbacks=CustomCallback ...
 
-This callback will be included in the generated config:
+.. note::
 
-.. code-block:: yaml
+    This shorthand notation is only supported in the shell and not inside a configuration file. The configuration file
+    generated by calling the previous command with ``--print_config`` will have the ``class_path`` notation.
+
+    .. code-block:: yaml
+
+        trainer:
+          callbacks:
+            - class_path: your_class_path.CustomCallback
+              init_args:
+                ...
 
-    trainer:
-      callbacks:
-        - class_path: your_class_path.CustomCallback
-          init_args:
-            ...
 
 Multiple models and/or datasets
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 In the previous examples :class:`~pytorch_lightning.utilities.cli.LightningCLI` works only for a single model and
 datamodule class. However, there are many cases in which the objective is to easily be able to run many experiments for
-multiple models and datasets. For these cases the tool can be configured such that a model and/or a datamodule is
+multiple models and datasets.
+
+The model argument can be left unset if a model has been registered first, this is particularly interesting for library
+authors who want to provide their users a range of models to choose from:
+
+.. code-block:: python
+
+    import flash.image
+    from pytorch_lightning.utilities.cli import MODEL_REGISTRY
+
+
+    @MODEL_REGISTRY
+    class SimCLR(LightningModule):
+        ...
+
+
+    # register all `LightningModule` subclasses from a package
+    MODEL_REGISTRY.register_classes(flash.image, LightningModule)
+    # print(MODEL_REGISTRY)
+    # >>> Registered objects: ['SimCLR', 'ImageClassifier', 'ObjectDetector', 'StyleTransfer', ...]
+
+    cli = LightningCLI()
+
+.. code-block:: bash
+
+    $ python trainer.py fit --model=SimCLR --model.feat_dim=64
+
+.. note::
+
+    This shorthand notation is only supported in the shell and not inside a configuration file. The configuration file
+    generated by calling the previous command with ``--print_config`` will have the ``class_path`` notation described
+    below.
+
+Additionally, the tool can be configured such that a model and/or a datamodule is
 specified by an import path and init arguments. For example, with a tool implemented as:
 
 .. code-block:: python
@@ -750,7 +787,7 @@ A corresponding example of the config file would be:
 
 .. note::
 
-    This short-hand notation is only supported in the shell and not inside a configuration file. The configuration file
+    This shorthand notation is only supported in the shell and not inside a configuration file. The configuration file
     generated by calling the previous command with ``--print_config`` will have the ``class_path`` notation.
 
 Furthermore, you can register your own optimizers and/or learning rate schedulers as follows:
@@ -894,8 +931,8 @@ Notes related to reproducibility
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 The topic of reproducibility is complex and it is impossible to guarantee reproducibility by just providing a class that
-people can use in unexpected ways. Nevertheless :class:`~pytorch_lightning.utilities.cli.LightningCLI` tries to give a
-framework and recommendations to make reproducibility simpler.
+people can use in unexpected ways. Nevertheless, the :class:`~pytorch_lightning.utilities.cli.LightningCLI` tries to
+give a framework and recommendations to make reproducibility simpler.
 
 When an experiment is run, it is good practice to use a stable version of the source code, either being a released
 package or at least a commit of some version controlled repository. For each run of a CLI the config file is

pytorch_lightning/utilities/cli.py

@@ -42,7 +42,7 @@
 
 
 class _Registry(dict):
-    def __call__(self, cls: Type, key: Optional[str] = None, override: bool = False) -> None:
+    def __call__(self, cls: Type, key: Optional[str] = None, override: bool = False) -> Type:
         """Registers a class mapped to a name.
 
         Args:
@@ -58,6 +58,7 @@ def __call__(self, cls: Type, key: Optional[str] = None, override: bool = False)
         if key in self and not override:
             raise MisconfigurationException(f"'{key}' is already present in the registry. HINT: Use `override=True`.")
         self[key] = cls
+        return cls
 
     def register_classes(self, module: ModuleType, base_cls: Type, override: bool = False) -> None:
         """This function is an utility to register all classes from a module."""
@@ -88,6 +89,8 @@ def __str__(self) -> str:
 CALLBACK_REGISTRY = _Registry()
 CALLBACK_REGISTRY.register_classes(pl.callbacks, pl.callbacks.Callback)
 
+MODEL_REGISTRY = _Registry()
+
 
 class LightningArgumentParser(ArgumentParser):
     """Extension of jsonargparse's ArgumentParser for pytorch-lightning."""
@@ -146,7 +149,7 @@ def add_lightning_class_args(
             if issubclass(lightning_class, Callback):
                 self.callback_keys.append(nested_key)
             if subclass_mode:
-                return self.add_subclass_arguments(lightning_class, nested_key, required=True)
+                return self.add_subclass_arguments(lightning_class, nested_key, fail_untyped=False, required=True)
             return self.add_class_arguments(
                 lightning_class, nested_key, fail_untyped=False, instantiate=not issubclass(lightning_class, Trainer)
             )
@@ -384,7 +387,7 @@ class LightningCLI:
 
     def __init__(
         self,
-        model_class: Union[Type[LightningModule], Callable[..., LightningModule]],
+        model_class: Optional[Union[Type[LightningModule], Callable[..., LightningModule]]] = None,
         datamodule_class: Optional[Union[Type[LightningDataModule], Callable[..., LightningDataModule]]] = None,
         save_config_callback: Optional[Type[SaveConfigCallback]] = SaveConfigCallback,
         save_config_filename: str = "config.yaml",
@@ -412,8 +415,9 @@ def __init__(
         .. warning:: ``LightningCLI`` is in beta and subject to change.
 
         Args:
-            model_class: :class:`~pytorch_lightning.core.lightning.LightningModule` class to train on or a callable
-                which returns a :class:`~pytorch_lightning.core.lightning.LightningModule` instance when called.
+            model_class: An optional :class:`~pytorch_lightning.core.lightning.LightningModule` class to train on or a
+                callable which returns a :class:`~pytorch_lightning.core.lightning.LightningModule` instance when
+                called. If ``None``, you can pass a registered model with ``--model=AModel``.
             datamodule_class: An optional :class:`~pytorch_lightning.core.datamodule.LightningDataModule` class or a
                 callable which returns a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` instance when
                 called.
@@ -438,17 +442,23 @@ def __init__(
             run: Whether subcommands should be added to run a :class:`~pytorch_lightning.trainer.trainer.Trainer`
                 method. If set to ``False``, the trainer and model classes will be instantiated only.
         """
-        self.model_class = model_class
         self.datamodule_class = datamodule_class
         self.save_config_callback = save_config_callback
         self.save_config_filename = save_config_filename
         self.save_config_overwrite = save_config_overwrite
         self.trainer_class = trainer_class
         self.trainer_defaults = trainer_defaults or {}
         self.seed_everything_default = seed_everything_default
-        self.subclass_mode_model = subclass_mode_model
         self.subclass_mode_data = subclass_mode_data
 
+        self.model_class = model_class
+        self._model_class = model_class
+        self.subclass_mode_model = subclass_mode_model
+        if model_class is None:
+            # used to differentiate between the original value and the processed value
+            self._model_class = LightningModule
+            self.subclass_mode_model = True
+
         main_kwargs, subparser_kwargs = self._setup_parser_kwargs(
             parser_kwargs or {},  # type: ignore  # github.com/python/mypy/issues/6463
             {"description": description, "env_prefix": env_prefix, "default_env": env_parse},
@@ -508,7 +518,12 @@ def add_core_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
         parser.set_choices("trainer.callbacks", CALLBACK_REGISTRY.classes, is_list=True)
         trainer_defaults = {"trainer." + k: v for k, v in self.trainer_defaults.items() if k != "callbacks"}
         parser.set_defaults(trainer_defaults)
-        parser.add_lightning_class_args(self.model_class, "model", subclass_mode=self.subclass_mode_model)
+
+        parser.add_lightning_class_args(self._model_class, "model", subclass_mode=self.subclass_mode_model)
+        if self.model_class is None and MODEL_REGISTRY:
+            # did not pass a model and there are models registered
+            parser.set_choices("model", MODEL_REGISTRY.classes)
+
         if self.datamodule_class is not None:
             parser.add_lightning_class_args(self.datamodule_class, "data", subclass_mode=self.subclass_mode_data)
 

tests/utilities/test_cli.py

@@ -22,6 +22,7 @@
 from io import StringIO
 from typing import List, Optional, Union
 from unittest import mock
+from unittest.mock import ANY
 
 import pytest
 import torch
@@ -39,6 +40,7 @@
     LightningArgumentParser,
     LightningCLI,
     LR_SCHEDULER_REGISTRY,
+    MODEL_REGISTRY,
     OPTIMIZER_REGISTRY,
     SaveConfigCallback,
 )
@@ -883,6 +885,35 @@ def test_registries(tmpdir):
         OPTIMIZER_REGISTRY.register_classes(torch.optim, torch.optim.Optimizer)
     OPTIMIZER_REGISTRY.register_classes(torch.optim, torch.optim.Optimizer, override=True)
 
+    # test `_Registry.__call__` returns the class
+    assert isinstance(CustomCallback(), CustomCallback)
+
+
+@MODEL_REGISTRY
+class TestModel(BoringModel):
+    def __init__(self, foo, bar=5):
+        super().__init__()
+        self.foo = foo
+        self.bar = bar
+
+
+MODEL_REGISTRY(cls=BoringModel)
+
+
+def test_lightning_cli_model_choices():
+    with mock.patch("sys.argv", ["any.py", "fit", "--model=BoringModel"]), mock.patch(
+        "pytorch_lightning.Trainer._fit_impl"
+    ) as run:
+        cli = LightningCLI(trainer_defaults={"fast_dev_run": 1})
+        assert isinstance(cli.model, BoringModel)
+        run.assert_called_once_with(cli.model, ANY, ANY, ANY)
+
+    with mock.patch("sys.argv", ["any.py", "--model=TestModel", "--model.foo", "123"]):
+        cli = LightningCLI(run=False)
+        assert isinstance(cli.model, TestModel)
+        assert cli.model.foo == 123
+        assert cli.model.bar == 5
+
 
 @pytest.mark.parametrize("use_class_path_callbacks", [False, True])
 def test_registries_resolution(use_class_path_callbacks):
@@ -895,6 +926,7 @@ def test_registries_resolution(use_class_path_callbacks):
         "--trainer.callbacks=LearningRateMonitor",
         "--trainer.callbacks.logging_interval=epoch",
         "--trainer.callbacks.log_momentum=True",
+        "--model=BoringModel",
         "--trainer.callbacks=ModelCheckpoint",
         "--trainer.callbacks.monitor=loss",
         "--lr_scheduler",
@@ -912,8 +944,9 @@ def test_registries_resolution(use_class_path_callbacks):
         extras = [Callback, Callback]
 
     with mock.patch("sys.argv", ["any.py"] + cli_args):
-        cli = LightningCLI(BoringModel, run=False)
+        cli = LightningCLI(run=False)
 
+    assert isinstance(cli.model, BoringModel)
     optimizers, lr_scheduler = cli.model.configure_optimizers()
     assert isinstance(optimizers[0], torch.optim.Adam)
     assert optimizers[0].param_groups[0]["lr"] == 0.0001