# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import json import os import pickle import sys from argparse import Namespace from contextlib import redirect_stdout from io import StringIO from typing import List, Optional, Union from unittest import mock import pytest import torch import yaml from packaging import version from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint from pytorch_lightning.plugins.environments import SLURMEnvironment from pytorch_lightning.utilities import _TPU_AVAILABLE from pytorch_lightning.utilities.cli import instantiate_class, LightningArgumentParser, LightningCLI, SaveConfigCallback from pytorch_lightning.utilities.imports import _TORCHVISION_AVAILABLE from tests.helpers import BoringDataModule, BoringModel from tests.helpers.runif import RunIf torchvision_version = version.parse("0") if _TORCHVISION_AVAILABLE: torchvision_version = version.parse(__import__("torchvision").__version__) @mock.patch("argparse.ArgumentParser.parse_args") def test_default_args(mock_argparse, tmpdir): """Tests default argument parser for Trainer""" mock_argparse.return_value = Namespace(**Trainer.default_attributes()) parser = LightningArgumentParser(add_help=False, parse_as_dict=False) args = parser.parse_args([]) args.max_epochs = 5 trainer = Trainer.from_argparse_args(args) assert isinstance(trainer, Trainer) assert trainer.max_epochs == 5 @pytest.mark.parametrize("cli_args", [["--accumulate_grad_batches=22"], ["--weights_save_path=./"], []]) def test_add_argparse_args_redefined(cli_args): """Redefines some default Trainer arguments via the cli and tests the Trainer initialization correctness. """ parser = LightningArgumentParser(add_help=False, parse_as_dict=False) parser.add_lightning_class_args(Trainer, None) args = parser.parse_args(cli_args) # make sure we can pickle args pickle.dumps(args) # Check few deprecated args are not in namespace: for depr_name in ("gradient_clip", "nb_gpu_nodes", "max_nb_epochs"): assert depr_name not in args trainer = Trainer.from_argparse_args(args=args) pickle.dumps(trainer) assert isinstance(trainer, Trainer) @pytest.mark.parametrize("cli_args", [["--callbacks=1", "--logger"], ["--foo", "--bar=1"]]) def test_add_argparse_args_redefined_error(cli_args, monkeypatch): """Asserts error raised in case of passing not default cli arguments.""" class _UnkArgError(Exception): pass def _raise(): raise _UnkArgError parser = LightningArgumentParser(add_help=False, parse_as_dict=False) parser.add_lightning_class_args(Trainer, None) monkeypatch.setattr(parser, "exit", lambda *args: _raise(), raising=True) with pytest.raises(_UnkArgError): parser.parse_args(cli_args) @pytest.mark.parametrize( ["cli_args", "expected"], [ ("--auto_lr_find=True --auto_scale_batch_size=power", dict(auto_lr_find=True, auto_scale_batch_size="power")), ( "--auto_lr_find any_string --auto_scale_batch_size ON", dict(auto_lr_find="any_string", auto_scale_batch_size=True), ), ("--auto_lr_find=Yes --auto_scale_batch_size=On", dict(auto_lr_find=True, auto_scale_batch_size=True)), ("--auto_lr_find Off --auto_scale_batch_size No", dict(auto_lr_find=False, auto_scale_batch_size=False)), ("--auto_lr_find TRUE --auto_scale_batch_size FALSE", dict(auto_lr_find=True, auto_scale_batch_size=False)), ("--tpu_cores=8", dict(tpu_cores=8)), ("--tpu_cores=1,", dict(tpu_cores="1,")), ("--limit_train_batches=100", dict(limit_train_batches=100)), ("--limit_train_batches 0.8", dict(limit_train_batches=0.8)), ("--weights_summary=null", dict(weights_summary=None)), ( "", dict( # These parameters are marked as Optional[...] in Trainer.__init__, # with None as default. They should not be changed by the argparse # interface. min_steps=None, max_steps=None, log_gpu_memory=None, distributed_backend=None, weights_save_path=None, resume_from_checkpoint=None, profiler=None, ), ), ], ) def test_parse_args_parsing(cli_args, expected): """Test parsing simple types and None optionals not modified.""" cli_args = cli_args.split(" ") if cli_args else [] parser = LightningArgumentParser(add_help=False, parse_as_dict=False) parser.add_lightning_class_args(Trainer, None) with mock.patch("sys.argv", ["any.py"] + cli_args): args = parser.parse_args() for k, v in expected.items(): assert getattr(args, k) == v if "tpu_cores" not in expected or _TPU_AVAILABLE: assert Trainer.from_argparse_args(args) @pytest.mark.parametrize( ["cli_args", "expected", "instantiate"], [ (["--gpus", "[0, 2]"], dict(gpus=[0, 2]), False), (["--tpu_cores=[1,3]"], dict(tpu_cores=[1, 3]), False), (['--accumulate_grad_batches={"5":3,"10":20}'], dict(accumulate_grad_batches={5: 3, 10: 20}), True), ], ) def test_parse_args_parsing_complex_types(cli_args, expected, instantiate): """Test parsing complex types.""" parser = LightningArgumentParser(add_help=False, parse_as_dict=False) parser.add_lightning_class_args(Trainer, None) with mock.patch("sys.argv", ["any.py"] + cli_args): args = parser.parse_args() for k, v in expected.items(): assert getattr(args, k) == v if instantiate: assert Trainer.from_argparse_args(args) @pytest.mark.parametrize(["cli_args", "expected_gpu"], [("--gpus 1", [0]), ("--gpus 0,", [0]), ("--gpus 0,1", [0, 1])]) def test_parse_args_parsing_gpus(monkeypatch, cli_args, expected_gpu): """Test parsing of gpus and instantiation of Trainer.""" monkeypatch.setattr("torch.cuda.device_count", lambda: 2) cli_args = cli_args.split(" ") if cli_args else [] parser = LightningArgumentParser(add_help=False, parse_as_dict=False) parser.add_lightning_class_args(Trainer, None) with mock.patch("sys.argv", ["any.py"] + cli_args): args = parser.parse_args() trainer = Trainer.from_argparse_args(args) assert trainer.data_parallel_device_ids == expected_gpu @pytest.mark.skipif( sys.version_info < (3, 7), reason="signature inspection while mocking is not working in Python < 3.7 despite autospec", ) @pytest.mark.parametrize( ["cli_args", "extra_args"], [ ({}, {}), (dict(logger=False), {}), (dict(logger=False), dict(logger=True)), (dict(logger=False), dict(checkpoint_callback=True)), ], ) def test_init_from_argparse_args(cli_args, extra_args): unknown_args = dict(unknown_arg=0) # unkown args in the argparser/namespace should be ignored with mock.patch("pytorch_lightning.Trainer.__init__", autospec=True, return_value=None) as init: trainer = Trainer.from_argparse_args(Namespace(**cli_args, **unknown_args), **extra_args) expected = dict(cli_args) expected.update(extra_args) # extra args should override any cli arg init.assert_called_with(trainer, **expected) # passing in unknown manual args should throw an error with pytest.raises(TypeError, match=r"__init__\(\) got an unexpected keyword argument 'unknown_arg'"): Trainer.from_argparse_args(Namespace(**cli_args), **extra_args, **unknown_args) class Model(LightningModule): def __init__(self, model_param: int): super().__init__() self.model_param = model_param def _model_builder(model_param: int) -> Model: return Model(model_param) def _trainer_builder( limit_train_batches: int, fast_dev_run: bool = False, callbacks: Optional[Union[List[Callback], Callback]] = None ) -> Trainer: return Trainer(limit_train_batches=limit_train_batches, fast_dev_run=fast_dev_run, callbacks=callbacks) @pytest.mark.parametrize(["trainer_class", "model_class"], [(Trainer, Model), (_trainer_builder, _model_builder)]) def test_lightning_cli(trainer_class, model_class, monkeypatch): """Test that LightningCLI correctly instantiates model, trainer and calls fit.""" expected_model = dict(model_param=7) expected_trainer = dict(limit_train_batches=100) def fit(trainer, model): for k, v in expected_model.items(): assert getattr(model, k) == v for k, v in expected_trainer.items(): assert getattr(trainer, k) == v save_callback = [x for x in trainer.callbacks if isinstance(x, SaveConfigCallback)] assert len(save_callback) == 1 save_callback[0].on_train_start(trainer, model) def on_train_start(callback, trainer, _): config_dump = callback.parser.dump(callback.config, skip_none=False) for k, v in expected_model.items(): assert f" {k}: {v}" in config_dump for k, v in expected_trainer.items(): assert f" {k}: {v}" in config_dump trainer.ran_asserts = True monkeypatch.setattr(Trainer, "fit", fit) monkeypatch.setattr(SaveConfigCallback, "on_train_start", on_train_start) with mock.patch("sys.argv", ["any.py", "--model.model_param=7", "--trainer.limit_train_batches=100"]): cli = LightningCLI(model_class, trainer_class=trainer_class, save_config_callback=SaveConfigCallback) assert hasattr(cli.trainer, "ran_asserts") and cli.trainer.ran_asserts def test_lightning_cli_args_callbacks(tmpdir): callbacks = [ dict( class_path="pytorch_lightning.callbacks.LearningRateMonitor", init_args=dict(logging_interval="epoch", log_momentum=True), ), dict(class_path="pytorch_lightning.callbacks.ModelCheckpoint", init_args=dict(monitor="NAME")), ] class TestModel(BoringModel): def on_fit_start(self): callback = [c for c in self.trainer.callbacks if isinstance(c, LearningRateMonitor)] assert len(callback) == 1 assert callback[0].logging_interval == "epoch" assert callback[0].log_momentum is True callback = [c for c in self.trainer.callbacks if isinstance(c, ModelCheckpoint)] assert len(callback) == 1 assert callback[0].monitor == "NAME" self.trainer.ran_asserts = True with mock.patch("sys.argv", ["any.py", f"--trainer.callbacks={json.dumps(callbacks)}"]): cli = LightningCLI(TestModel, trainer_defaults=dict(default_root_dir=str(tmpdir), fast_dev_run=True)) assert cli.trainer.ran_asserts def test_lightning_cli_configurable_callbacks(tmpdir): class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.add_lightning_class_args(LearningRateMonitor, "learning_rate_monitor") cli_args = [ f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", "--learning_rate_monitor.logging_interval=epoch", ] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = MyLightningCLI(BoringModel) callback = [c for c in cli.trainer.callbacks if isinstance(c, LearningRateMonitor)] assert len(callback) == 1 assert callback[0].logging_interval == "epoch" def test_lightning_cli_args_cluster_environments(tmpdir): plugins = [dict(class_path="pytorch_lightning.plugins.environments.SLURMEnvironment")] class TestModel(BoringModel): def on_fit_start(self): # Ensure SLURMEnvironment is set, instead of default LightningEnvironment assert isinstance(self.trainer.accelerator_connector._cluster_environment, SLURMEnvironment) self.trainer.ran_asserts = True with mock.patch("sys.argv", ["any.py", f"--trainer.plugins={json.dumps(plugins)}"]): cli = LightningCLI(TestModel, trainer_defaults=dict(default_root_dir=str(tmpdir), fast_dev_run=True)) assert cli.trainer.ran_asserts def test_lightning_cli_args(tmpdir): cli_args = [ f"--data.data_dir={tmpdir}", f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", "--trainer.weights_summary=null", "--seed_everything=1234", ] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = LightningCLI(BoringModel, BoringDataModule, trainer_defaults={"callbacks": [LearningRateMonitor()]}) assert cli.config["seed_everything"] == 1234 config_path = tmpdir / "lightning_logs" / "version_0" / "config.yaml" assert os.path.isfile(config_path) with open(config_path) as f: config = yaml.safe_load(f.read()) assert "model" not in config and "model" not in cli.config # no arguments to include assert config["data"] == cli.config["data"] assert config["trainer"] == cli.config["trainer"] def test_lightning_cli_save_config_cases(tmpdir): config_path = tmpdir / "config.yaml" cli_args = [f"--trainer.default_root_dir={tmpdir}", "--trainer.logger=False", "--trainer.fast_dev_run=1"] # With fast_dev_run!=False config should not be saved with mock.patch("sys.argv", ["any.py"] + cli_args): LightningCLI(BoringModel) assert not os.path.isfile(config_path) # With fast_dev_run==False config should be saved cli_args[-1] = "--trainer.max_epochs=1" with mock.patch("sys.argv", ["any.py"] + cli_args): LightningCLI(BoringModel) assert os.path.isfile(config_path) # If run again on same directory exception should be raised since config file already exists with mock.patch("sys.argv", ["any.py"] + cli_args), pytest.raises(RuntimeError): LightningCLI(BoringModel) def test_lightning_cli_config_and_subclass_mode(tmpdir): config = dict( model=dict(class_path="tests.helpers.BoringModel"), data=dict(class_path="tests.helpers.BoringDataModule", init_args=dict(data_dir=str(tmpdir))), trainer=dict(default_root_dir=str(tmpdir), max_epochs=1, weights_summary=None), ) config_path = tmpdir / "config.yaml" with open(config_path, "w") as f: f.write(yaml.dump(config)) with mock.patch("sys.argv", ["any.py", "--config", str(config_path)]): cli = LightningCLI( BoringModel, BoringDataModule, subclass_mode_model=True, subclass_mode_data=True, trainer_defaults={"callbacks": LearningRateMonitor()}, ) config_path = tmpdir / "lightning_logs" / "version_0" / "config.yaml" assert os.path.isfile(config_path) with open(config_path) as f: config = yaml.safe_load(f.read()) assert config["model"] == cli.config["model"] assert config["data"] == cli.config["data"] assert config["trainer"] == cli.config["trainer"] def any_model_any_data_cli(): LightningCLI(LightningModule, LightningDataModule, subclass_mode_model=True, subclass_mode_data=True) def test_lightning_cli_help(): cli_args = ["any.py", "--help"] out = StringIO() with mock.patch("sys.argv", cli_args), redirect_stdout(out), pytest.raises(SystemExit): any_model_any_data_cli() out = out.getvalue() assert "--print_config" in out assert "--config" in out assert "--seed_everything" in out assert "--model.help" in out assert "--data.help" in out skip_params = {"self"} for param in inspect.signature(Trainer.__init__).parameters.keys(): if param not in skip_params: assert f"--trainer.{param}" in out cli_args = ["any.py", "--data.help=tests.helpers.BoringDataModule"] out = StringIO() with mock.patch("sys.argv", cli_args), redirect_stdout(out), pytest.raises(SystemExit): any_model_any_data_cli() assert "--data.init_args.data_dir" in out.getvalue() def test_lightning_cli_print_config(): cli_args = [ "any.py", "--seed_everything=1234", "--model=tests.helpers.BoringModel", "--data=tests.helpers.BoringDataModule", "--print_config", ] out = StringIO() with mock.patch("sys.argv", cli_args), redirect_stdout(out), pytest.raises(SystemExit): any_model_any_data_cli() outval = yaml.safe_load(out.getvalue()) assert outval["seed_everything"] == 1234 assert outval["model"]["class_path"] == "tests.helpers.BoringModel" assert outval["data"]["class_path"] == "tests.helpers.BoringDataModule" def test_lightning_cli_submodules(tmpdir): class MainModule(BoringModel): def __init__(self, submodule1: LightningModule, submodule2: LightningModule, main_param: int = 1): super().__init__() self.submodule1 = submodule1 self.submodule2 = submodule2 config = """model: main_param: 2 submodule1: class_path: tests.helpers.BoringModel submodule2: class_path: tests.helpers.BoringModel """ config_path = tmpdir / "config.yaml" with open(config_path, "w") as f: f.write(config) cli_args = [f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", f"--config={str(config_path)}"] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = LightningCLI(MainModule) assert cli.config["model"]["main_param"] == 2 assert isinstance(cli.model.submodule1, BoringModel) assert isinstance(cli.model.submodule2, BoringModel) @pytest.mark.skipif(torchvision_version < version.parse("0.8.0"), reason="torchvision>=0.8.0 is required") def test_lightning_cli_torch_modules(tmpdir): class TestModule(BoringModel): def __init__(self, activation: torch.nn.Module = None, transform: Optional[List[torch.nn.Module]] = None): super().__init__() self.activation = activation self.transform = transform config = """model: activation: class_path: torch.nn.LeakyReLU init_args: negative_slope: 0.2 transform: - class_path: torchvision.transforms.Resize init_args: size: 64 - class_path: torchvision.transforms.CenterCrop init_args: size: 64 """ config_path = tmpdir / "config.yaml" with open(config_path, "w") as f: f.write(config) cli_args = [f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", f"--config={str(config_path)}"] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = LightningCLI(TestModule) assert isinstance(cli.model.activation, torch.nn.LeakyReLU) assert cli.model.activation.negative_slope == 0.2 assert len(cli.model.transform) == 2 assert all(isinstance(v, torch.nn.Module) for v in cli.model.transform) class BoringModelRequiredClasses(BoringModel): def __init__(self, num_classes: int, batch_size: int = 8): super().__init__() self.num_classes = num_classes self.batch_size = batch_size class BoringDataModuleBatchSizeAndClasses(BoringDataModule): def __init__(self, batch_size: int = 8): super().__init__() self.batch_size = batch_size self.num_classes = 5 # only available after instantiation def test_lightning_cli_link_arguments(tmpdir): class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.link_arguments("data.batch_size", "model.batch_size") parser.link_arguments("data.num_classes", "model.num_classes", apply_on="instantiate") cli_args = [f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", "--data.batch_size=12"] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = MyLightningCLI(BoringModelRequiredClasses, BoringDataModuleBatchSizeAndClasses) assert cli.model.batch_size == 12 assert cli.model.num_classes == 5 class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.link_arguments("data.batch_size", "model.init_args.batch_size") parser.link_arguments("data.num_classes", "model.init_args.num_classes", apply_on="instantiate") cli_args[-1] = "--model=tests.utilities.test_cli.BoringModelRequiredClasses" with mock.patch("sys.argv", ["any.py"] + cli_args): cli = MyLightningCLI(BoringModelRequiredClasses, BoringDataModuleBatchSizeAndClasses, subclass_mode_model=True) assert cli.model.batch_size == 8 assert cli.model.num_classes == 5 class EarlyExitTestModel(BoringModel): def on_fit_start(self): raise KeyboardInterrupt() @pytest.mark.parametrize("logger", (False, True)) @pytest.mark.parametrize( "trainer_kwargs", ( dict(accelerator="ddp_cpu"), dict(accelerator="ddp_cpu", plugins="ddp_find_unused_parameters_false"), pytest.param({"tpu_cores": 1}, marks=RunIf(tpu=True)), ), ) def test_cli_ddp_spawn_save_config_callback(tmpdir, logger, trainer_kwargs): with mock.patch("sys.argv", ["any.py"]), pytest.raises(KeyboardInterrupt): LightningCLI( EarlyExitTestModel, trainer_defaults={ "default_root_dir": str(tmpdir), "logger": logger, "max_steps": 1, "max_epochs": 1, **trainer_kwargs, }, ) if logger: config_dir = tmpdir / "lightning_logs" # no more version dirs should get created assert os.listdir(config_dir) == ["version_0"] config_path = config_dir / "version_0" / "config.yaml" else: config_path = tmpdir / "config.yaml" assert os.path.isfile(config_path) def test_cli_config_overwrite(tmpdir): trainer_defaults = {"default_root_dir": str(tmpdir), "logger": False, "max_steps": 1, "max_epochs": 1} with mock.patch("sys.argv", ["any.py"]): LightningCLI(BoringModel, trainer_defaults=trainer_defaults) with mock.patch("sys.argv", ["any.py"]), pytest.raises(RuntimeError, match="Aborting to avoid overwriting"): LightningCLI(BoringModel, trainer_defaults=trainer_defaults) with mock.patch("sys.argv", ["any.py"]): LightningCLI(BoringModel, save_config_overwrite=True, trainer_defaults=trainer_defaults) def test_lightning_cli_optimizer(tmpdir): class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.add_optimizer_args(torch.optim.Adam) cli_args = [f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1"] match = ( "BoringModel.configure_optimizers` will be overridden by " "`MyLightningCLI.add_configure_optimizers_method_to_model`" ) with mock.patch("sys.argv", ["any.py"] + cli_args), pytest.warns(UserWarning, match=match): cli = MyLightningCLI(BoringModel) assert cli.model.configure_optimizers is not BoringModel.configure_optimizers assert len(cli.trainer.optimizers) == 1 assert isinstance(cli.trainer.optimizers[0], torch.optim.Adam) assert len(cli.trainer.lr_schedulers) == 0 def test_lightning_cli_optimizer_and_lr_scheduler(tmpdir): class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.add_optimizer_args(torch.optim.Adam) parser.add_lr_scheduler_args(torch.optim.lr_scheduler.ExponentialLR) cli_args = [f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", "--lr_scheduler.gamma=0.8"] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = MyLightningCLI(BoringModel) assert cli.model.configure_optimizers is not BoringModel.configure_optimizers assert len(cli.trainer.optimizers) == 1 assert isinstance(cli.trainer.optimizers[0], torch.optim.Adam) assert len(cli.trainer.lr_schedulers) == 1 assert isinstance(cli.trainer.lr_schedulers[0]["scheduler"], torch.optim.lr_scheduler.ExponentialLR) assert cli.trainer.lr_schedulers[0]["scheduler"].gamma == 0.8 def test_lightning_cli_optimizer_and_lr_scheduler_subclasses(tmpdir): class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.add_optimizer_args((torch.optim.SGD, torch.optim.Adam)) parser.add_lr_scheduler_args((torch.optim.lr_scheduler.StepLR, torch.optim.lr_scheduler.ExponentialLR)) optimizer_arg = dict(class_path="torch.optim.Adam", init_args=dict(lr=0.01)) lr_scheduler_arg = dict(class_path="torch.optim.lr_scheduler.StepLR", init_args=dict(step_size=50)) cli_args = [ f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", f"--optimizer={json.dumps(optimizer_arg)}", f"--lr_scheduler={json.dumps(lr_scheduler_arg)}", ] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = MyLightningCLI(BoringModel) assert len(cli.trainer.optimizers) == 1 assert isinstance(cli.trainer.optimizers[0], torch.optim.Adam) assert len(cli.trainer.lr_schedulers) == 1 assert isinstance(cli.trainer.lr_schedulers[0]["scheduler"], torch.optim.lr_scheduler.StepLR) assert cli.trainer.lr_schedulers[0]["scheduler"].step_size == 50 def test_lightning_cli_optimizers_and_lr_scheduler_with_link_to(tmpdir): class MyLightningCLI(LightningCLI): def add_arguments_to_parser(self, parser): parser.add_optimizer_args(torch.optim.Adam, nested_key="optim1", link_to="model.optim1") parser.add_optimizer_args((torch.optim.ASGD, torch.optim.SGD), nested_key="optim2", link_to="model.optim2") parser.add_lr_scheduler_args(torch.optim.lr_scheduler.ExponentialLR, link_to="model.scheduler") class TestModel(BoringModel): def __init__(self, optim1: dict, optim2: dict, scheduler: dict): super().__init__() self.optim1 = instantiate_class(self.parameters(), optim1) self.optim2 = instantiate_class(self.parameters(), optim2) self.scheduler = instantiate_class(self.optim1, scheduler) cli_args = [ f"--trainer.default_root_dir={tmpdir}", "--trainer.max_epochs=1", "--optim2.class_path=torch.optim.SGD", "--optim2.init_args.lr=0.01", "--lr_scheduler.gamma=0.2", ] with mock.patch("sys.argv", ["any.py"] + cli_args): cli = MyLightningCLI(TestModel) assert isinstance(cli.model.optim1, torch.optim.Adam) assert isinstance(cli.model.optim2, torch.optim.SGD) assert isinstance(cli.model.scheduler, torch.optim.lr_scheduler.ExponentialLR) @pytest.mark.parametrize("run", (False, True)) def test_lightning_cli_disabled_run(run): with mock.patch("sys.argv", ["any.py"]), mock.patch("pytorch_lightning.Trainer.fit") as fit_mock: cli = LightningCLI(BoringModel, run=run) fit_mock.call_count == run assert isinstance(cli.trainer, Trainer) assert isinstance(cli.model, LightningModule)