LightningCLI natively support callback list append

CHANGELOG.md

@@ -97,6 +97,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed `pytorch_lightning.core.lightning` to `pytorch_lightning.core.module` ([#12740](https://github.com/PyTorchLightning/pytorch-lightning/pull/12740))
 
 
+- `LightningCLI` changed to use jsonargparse native support for list append ([#13129](https://github.com/PyTorchLightning/pytorch-lightning/pull/13129))
+
+
 -
 
 ### Deprecated

docs/source/cli/lightning_cli_advanced_3.rst

@@ -119,11 +119,11 @@ The argument's order matters and the user needs to pass the arguments in the fol
 .. code-block:: bash
 
     $ python ... \
-        --trainer.callbacks={CALLBACK_1_NAME} \
+        --trainer.callbacks+={CALLBACK_1_NAME} \
         --trainer.callbacks.{CALLBACK_1_ARGS_1}=... \
         --trainer.callbacks.{CALLBACK_1_ARGS_2}=... \
         ...
-        --trainer.callbacks={CALLBACK_N_NAME} \
+        --trainer.callbacks+={CALLBACK_N_NAME} \
         --trainer.callbacks.{CALLBACK_N_ARGS_1}=... \
         ...
 
@@ -132,9 +132,9 @@ Here is an example:
 .. code-block:: bash
 
     $ python ... \
-        --trainer.callbacks=EarlyStopping \
+        --trainer.callbacks+=EarlyStopping \
         --trainer.callbacks.patience=5 \
-        --trainer.callbacks=LearningRateMonitor \
+        --trainer.callbacks+=LearningRateMonitor \
         --trainer.callbacks.logging_interval=epoch
 
 Lightning provides a mechanism for you to add your own callbacks and benefit from the command line simplification
@@ -154,12 +154,12 @@ as described above:
 
 .. code-block:: bash
 
-    $  python ... --trainer.callbacks=CustomCallback ...
+    $  python ... --trainer.callbacks+=CustomCallback ...
 
 .. 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.
+    This shorthand notation is also supported inside a configuration file. The configuration file
+    generated by calling the previous command with ``--print_config`` will have the full ``class_path`` notation.
 
     .. code-block:: yaml
 

pytorch_lightning/utilities/cli.py

@@ -15,14 +15,11 @@
 
 import inspect
 import os
-import sys
 from functools import partial, update_wrapper
 from types import MethodType, ModuleType
 from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Type, Union
-from unittest import mock
 
 import torch
-import yaml
 from torch.optim import Optimizer
 
 import pytorch_lightning as pl
@@ -35,13 +32,11 @@
 from pytorch_lightning.utilities.rank_zero import _warn, rank_zero_deprecation, rank_zero_warn
 from pytorch_lightning.utilities.seed import _select_seed_randomly
 
-_JSONARGPARSE_SIGNATURES_AVAILABLE = _RequirementAvailable("jsonargparse[signatures]>=4.7.1")
+_JSONARGPARSE_SIGNATURES_AVAILABLE = _RequirementAvailable("jsonargparse[signatures]>=4.8.0.dev1")
 
 if _JSONARGPARSE_SIGNATURES_AVAILABLE:
     import docstring_parser
     from jsonargparse import ActionConfigFile, ArgumentParser, class_from_function, Namespace, set_config_read_mode
-    from jsonargparse.typehints import get_all_subclass_paths
-    from jsonargparse.util import import_object
 
     set_config_read_mode(fsspec_enabled=True)
 else:
@@ -255,73 +250,6 @@ def add_lr_scheduler_args(
             self.add_class_arguments(lr_scheduler_class, nested_key, sub_configs=True, **kwargs)
         self._lr_schedulers[nested_key] = (lr_scheduler_class, link_to)
 
-    def parse_args(self, *args: Any, **kwargs: Any) -> Dict[str, Any]:
-        argv = sys.argv
-        nested_key = "trainer.callbacks"
-        if any(arg.startswith(f"--{nested_key}") for arg in argv):
-            classes = tuple(import_object(x) for x in get_all_subclass_paths(Callback))
-            argv = self._convert_argv_issue_85(classes, nested_key, argv)
-        with mock.patch("sys.argv", argv):
-            return super().parse_args(*args, **kwargs)
-
-    @staticmethod
-    def _convert_argv_issue_85(classes: Tuple[Type, ...], nested_key: str, argv: List[str]) -> List[str]:
-        """Placeholder for https://github.com/omni-us/jsonargparse/issues/85.
-
-        Adds support for shorthand notation for ``List[object]`` arguments.
-        """
-        passed_args, clean_argv = [], []
-        passed_configs = {}
-        argv_key = f"--{nested_key}"
-        # get the argv args for this nested key
-        i = 0
-        while i < len(argv):
-            arg = argv[i]
-            if arg.startswith(argv_key):
-                if "=" in arg:
-                    key, value = arg.split("=")
-                else:
-                    key = arg
-                    i += 1
-                    value = argv[i]
-                if "class_path" in value:
-                    # the user passed a config as a dict
-                    passed_configs[key] = yaml.safe_load(value)
-                else:
-                    passed_args.append((key, value))
-            else:
-                clean_argv.append(arg)
-            i += 1
-        # generate the associated config file
-        config = []
-        i, n = 0, len(passed_args)
-        while i < n - 1:
-            ki, vi = passed_args[i]
-            # convert class name to class path
-            for cls in classes:
-                if cls.__name__ == vi:
-                    cls_type = cls
-                    break
-            else:
-                raise ValueError(f"Could not generate a config for {repr(vi)}")
-            config.append(_global_add_class_path(cls_type))
-            # get any init args
-            j = i + 1  # in case the j-loop doesn't run
-            for j in range(i + 1, n):
-                kj, vj = passed_args[j]
-                if ki == kj:
-                    break
-                if kj.startswith(ki):
-                    init_arg_name = kj.split(".")[-1]
-                    config[-1]["init_args"][init_arg_name] = vj
-            i = j
-        # update at the end to preserve the order
-        for k, v in passed_configs.items():
-            config.extend(v)
-        if not config:
-            return clean_argv
-        return clean_argv + [argv_key, str(config)]
-
 
 class SaveConfigCallback(Callback):
     """Saves a LightningCLI config to the log_dir when training starts.

requirements/extra.txt

@@ -4,6 +4,6 @@ matplotlib>3.1, <=3.5.1
 torchtext>=0.9.*, <=0.12.0
 omegaconf>=2.0.5, <=2.1.*
 hydra-core>=1.0.5, <=1.1.*
-jsonargparse[signatures]>=4.7.1, <=4.7.1
+jsonargparse[signatures]>=4.8.0.dev1
 gcsfs>=2021.5.0, <=2022.2.0
 rich>=10.2.2,!=10.15.*, <=12.0.0

tests/utilities/test_cli.py

@@ -1046,19 +1046,20 @@ def test_lightning_cli_datamodule_short_arguments():
 
 
 @pytest.mark.parametrize("use_class_path_callbacks", [False, True])
-def test_registries_resolution(use_class_path_callbacks):
+def test_callbacks_append(use_class_path_callbacks):
 
     """This test validates registries are used when simplified command line are being used."""
     cli_args = [
         "--optimizer",
         "Adam",
         "--optimizer.lr",
         "0.0001",
-        "--trainer.callbacks=LearningRateMonitor",
+        "--trainer.callbacks+=LearningRateMonitor",
         "--trainer.callbacks.logging_interval=epoch",
         "--trainer.callbacks.log_momentum=True",
         "--model=BoringModel",
-        "--trainer.callbacks=ModelCheckpoint",
+        "--trainer.callbacks+",
+        "ModelCheckpoint",
         "--trainer.callbacks.monitor=loss",
         "--lr_scheduler",
         "StepLR",
@@ -1071,7 +1072,7 @@ def test_registries_resolution(use_class_path_callbacks):
             {"class_path": "pytorch_lightning.callbacks.Callback"},
             {"class_path": "pytorch_lightning.callbacks.Callback", "init_args": {}},
         ]
-        cli_args += [f"--trainer.callbacks={json.dumps(callbacks)}"]
+        cli_args += [f"--trainer.callbacks+={json.dumps(callbacks)}"]
         extras = [Callback, Callback]
 
     with mock.patch("sys.argv", ["any.py"] + cli_args), mock_subclasses(LightningModule, BoringModel):
@@ -1088,79 +1089,6 @@ def test_registries_resolution(use_class_path_callbacks):
     assert all(t in callback_types for t in expected)
 
 
-def test_argv_transformation_noop():
-    base = ["any.py", "--trainer.max_epochs=1"]
-    argv = LightningArgumentParser._convert_argv_issue_85(CALLBACK_REGISTRY.classes, "trainer.callbacks", base)
-    assert argv == base
-
-
-def test_argv_transformation_single_callback():
-    base = ["any.py", "--trainer.max_epochs=1"]
-    input = base + ["--trainer.callbacks=ModelCheckpoint", "--trainer.callbacks.monitor=val_loss"]
-    callbacks = [
-        {
-            "class_path": "pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
-            "init_args": {"monitor": "val_loss"},
-        }
-    ]
-    expected = base + ["--trainer.callbacks", str(callbacks)]
-    _populate_registries(False)
-    argv = LightningArgumentParser._convert_argv_issue_85(CALLBACK_REGISTRY.classes, "trainer.callbacks", input)
-    assert argv == expected
-
-
-def test_argv_transformation_multiple_callbacks():
-    base = ["any.py", "--trainer.max_epochs=1"]
-    input = base + [
-        "--trainer.callbacks=ModelCheckpoint",
-        "--trainer.callbacks.monitor=val_loss",
-        "--trainer.callbacks=ModelCheckpoint",
-        "--trainer.callbacks.monitor=val_acc",
-    ]
-    callbacks = [
-        {
-            "class_path": "pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
-            "init_args": {"monitor": "val_loss"},
-        },
-        {
-            "class_path": "pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
-            "init_args": {"monitor": "val_acc"},
-        },
-    ]
-    expected = base + ["--trainer.callbacks", str(callbacks)]
-    _populate_registries(False)
-    argv = LightningArgumentParser._convert_argv_issue_85(CALLBACK_REGISTRY.classes, "trainer.callbacks", input)
-    assert argv == expected
-
-
-def test_argv_transformation_multiple_callbacks_with_config():
-    base = ["any.py", "--trainer.max_epochs=1"]
-    nested_key = "trainer.callbacks"
-    input = base + [
-        f"--{nested_key}=ModelCheckpoint",
-        f"--{nested_key}.monitor=val_loss",
-        f"--{nested_key}=ModelCheckpoint",
-        f"--{nested_key}.monitor=val_acc",
-        f"--{nested_key}=[{{'class_path': 'pytorch_lightning.callbacks.Callback'}}]",
-    ]
-    callbacks = [
-        {
-            "class_path": "pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
-            "init_args": {"monitor": "val_loss"},
-        },
-        {
-            "class_path": "pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint",
-            "init_args": {"monitor": "val_acc"},
-        },
-        {"class_path": "pytorch_lightning.callbacks.Callback"},
-    ]
-    expected = base + ["--trainer.callbacks", str(callbacks)]
-    nested_key = "trainer.callbacks"
-    _populate_registries(False)
-    argv = LightningArgumentParser._convert_argv_issue_85(CALLBACK_REGISTRY.classes, nested_key, input)
-    assert argv == expected
-
-
 def test_optimizers_and_lr_schedulers_reload(tmpdir):
     base = ["any.py", "--trainer.max_epochs=1"]
     input = base + [