Update defaults for WandbLogger's run name and project name

[awaelchli]

What does this PR do?

Fixes #14054

Script to test the behavior if you want to play with it:

import os

import torch
from torch.utils.data import DataLoader, Dataset

from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.loggers import WandbLogger


class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class BoringModel(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):
        loss = self(batch).sum()
        self.log("train_loss", loss)
        return {"loss": loss}

    def on_train_start(self) -> None:
        print(self.trainer.logger.save_dir)
        print(self.trainer.logger.name)
        print(self.trainer.logger.version)

    def configure_optimizers(self):
        return torch.optim.SGD(self.layer.parameters(), lr=0.1)


def run():
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)

    model = BoringModel()
    logger = WandbLogger(project="this")
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        limit_train_batches=1,
        limit_val_batches=1,
        limit_test_batches=1,
        num_sanity_val_steps=0,
        max_epochs=1,
        enable_model_summary=False,
        logger=logger,
    )
    trainer.fit(model, train_dataloaders=train_data)


if __name__ == "__main__":
    run()

Folder structure:

Wandb online record:

Does your PR introduce any breaking changes? If yes, please list them.

Yes, but they are necessary to satisfy the users.

• name: used to return the wandb.Run.name, but now returns the project name.
• Since name can't be None, we had to use a default name for the project name, and setting it to "lightning_logs" makes it more consistent with the other loggers.

Before submitting

• Was this discussed/approved via a GitHub issue? (not for typos and docs)
• Did you read the contributor guideline, Pull Request section?
• Did you make sure your PR does only one thing, instead of bundling different changes together?
• Did you make sure to update the documentation with your changes? (if necessary)
• Did you write any new necessary tests? (not for typos and docs)
• Did you verify new and existing tests pass locally with your changes?
• Did you update the CHANGELOG? (not for typos, docs, test updates, or internal minor changes/refactorings)

PR review

Anyone in the community is free to review the PR once the tests have passed.
Before you start reviewing make sure you have read Review guidelines. In short, see the following bullet-list:

• Is this pull request ready for review? (if not, please submit in draft mode)
• Check that all items from Before submitting are resolved
• Make sure the title is self-explanatory and the description concisely explains the PR
• Add labels and milestones (and optionally projects) to the PR so it can be classified

Did you have fun?

I made sure I had fun coding 🙃

cc @tchaton @rohitgr7 @Borda @justusschock @awaelchli @morganmcg1 @borisdayma @scottire @manangoel99 @akihironitta

[awaelchli]

gatekeeper rerun