Stepwise LR scheduler

[01AbhiSingh]

What does this PR do?

Fixes #<17544>

Hii @awaelchli. Can you please verify the changes I made. If they are correct then i will take up and correct any failing tests also.

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📚 Documentation preview 📚: https://pytorch-lightning--20211.org.readthedocs.build/en/20211/

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 81%. Comparing base (5be58f6) to head (3c48c9e).
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Flag	BASE (5be58f6)	HEAD (3c48c9e)
cpu	147	21
lightning	106	16
pytest	87	2
python3.9	43	6
python3.10	42	6
lightning_fabric	25	0
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python3.11	42	6
python3.12	20	3
pytorch2.1	38	12
pytest-full	64	21
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pytorch_lightning	20	7
pytorch2.2	9	3
pytorch2.4	8	3

Additional details and impacted files

@@            Coverage Diff            @@
##           master   #20211     +/-   ##
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- Coverage      89%      81%     -8%     
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  Files         267      264      -3     
  Lines       23084    23029     -55     
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- Hits        20585    18618   -1967     
- Misses       2499     4411   +1912     

[01AbhiSingh]

Hii @Borda. Do I need to make any kind of changes in the PR ?

[lantiga]

This looks good, thank you for the contribution @01AbhiSingh

Ideally we could add a test to verify the behavior described in #17544. The current test suite can't detect the current change and this is usually a sign of insufficient coverage. Would you be willing to contribute such test?

[01AbhiSingh]

Yes, sure let me look into it.

[01AbhiSingh]

Hi @lantiga , Do you want a new test written from scratch or need me to make the necessary changes in a preexisting file? All the tests have been passed. If the changes need to be made in a preexisting file, it would be very helpful if you could point out the test in which I need to make the changes, as all the tests have been passed, and due to that, I can't find the test.

[lantiga]

hey @01AbhiSingh sorry for the wait

You can take inspiration from:

pytorch-lightning/tests/tests_pytorch/trainer/optimization/test_optimizers.py

Line 459 in b0aa504

def test_lr_scheduler_epoch_step_frequency(mocked_sched, check_val_every_n_epoch, tmp_path):

and add a new test where scheduling goes across epoch boundaries. Maybe @falckt can help too?

[lantiga]

Hey @01AbhiSingh will you have time to add a test?

[01AbhiSingh]

Hey @01AbhiSingh will you have time to add a test?

Yes, Today's my last exam after that I will add the test asap. Sorry for the delay.

[lantiga]

@01AbhiSingh just checking, no pressure :-)

[01AbhiSingh]

Hii @lantiga. please check whether this is correct or not. What kind of changes I need to make to this code ?

def test_lr_scheduler_step_across_epoch_boundaries(mocked_sched, tmp_path):
    class StepAcrossEpochsModel(LightningModule):
        def __init__(self):
            super().__init__()
            self.layer = torch.nn.Linear(32, 2)

        def forward(self, x):
            return self.layer(x)

        def training_step(self, batch, batch_idx):
            return {"loss": torch.tensor(0.1, requires_grad=True)}

        def configure_optimizers(self):
            optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
            return {
                "optimizer": optimizer,
                "lr_scheduler": {
                    "scheduler": scheduler,
                    "interval": "step",
                    "frequency": 5,  # Scheduler steps every 5 iterations
                },
            }

    model = StepAcrossEpochsModel()

    # Trainer configuration for cross-epoch testing
    trainer = Trainer(
        default_root_dir=tmp_path,
        limit_train_batches=7,  # More than `frequency` iterations per epoch
        max_epochs=3,  # Test across multiple epochs
    )

    # Fit the model
    trainer.fit(model)

    # Calculate the total number of steps (iterations) and expected scheduler calls
    total_steps = 7 * 3  # Total iterations (7 batches per epoch * 3 epochs)
    expected_steps = total_steps // 5  # Scheduler steps every 5 iterations

    # Assert that the scheduler was called the expected number of times
    assert mocked_sched.call_count == expected_steps

[lantiga]

@01AbhiSingh the test looks good! feel free to add it to test_optimizers.py and we should be ok

[01AbhiSingh]

Done. sorry I was bogged down by few things. But, a lot of new PRs for all those issues coming your way now.

[lantiga]

there's an issue with mocked_sched that is undefined in the test, other than that things look good

[01AbhiSingh]

there's an issue with mocked_sched that is undefined in the test, other than that things look good

do you mean I need to add fixture to the test function's parameters.

@pytest.fixture
def mocked_sched(mocker):
    return mocker.patch('torch.optim.lr_scheduler.StepLR.step')

[lantiga]

That's right, you can also decorate the test with

from unittest.mock import patch

...

@patch("torch.optim.lr_scheduler.StepLR.step")
def test_lr_scheduler_step_across_epoch_boundaries(mocked_sched, tmp_path):
    ...

this should work

[01AbhiSingh]

Done please check

[lantiga]

Hey @01AbhiSingh can you import LightningModule here?

https://github.com/Lightning-AI/pytorch-lightning/pull/20211/files#diff-3c3f104dbdd06271c9e6e6d4fdf61398458148412401dd55a9bac1e9b5f913a8R19

Change:

from lightning.pytorch import Trainer

to

from lightning.pytorch import Trainer, LightningModule

this should fix the failing test