When is `stage is None` in pytorch lightning?

Question

Some official pytorch lightning docs have code that refer to stage as Optional[str] with for example the following code

import pytorch_lightning as pl
from torch.utils.data import random_split, DataLoader

# Note - you must have torchvision installed for this example
from torchvision.datasets import MNIST
from torchvision import transforms


class MNISTDataModule(pl.LightningDataModule):
    def __init__(self, data_dir: str = "./"):
        super().__init__()
        self.data_dir = data_dir
        self.transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

    def prepare_data(self):
        # download
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None):

        # Assign train/val datasets for use in dataloaders
        if stage == "fit" or stage is None:
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)

        if stage == "predict" or stage is None:
            self.mnist_predict = MNIST(self.data_dir, train=False, transform=self.transform)

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=32)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=32)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=32)

    def predict_dataloader(self):
        return DataLoader(self.mnist_predict, batch_size=32)

When does stage take the value of None? I could find no docs describing this.

Answer 1

The Trainer will never send stage=None to the setup hook, or any of the other hooks that take this argument. The type is annotated optional and the default value is None for historical reasons. The values will always be one of "fit", "validate", "test", "predict".

There is an RFC to change this to a required argument to avoid confusion. The link provides some more context why it has been like this for the past.