Fix resuming the tqdm progress bar

[awaelchli]

What does this PR do?

The changes in #12889 have caused #13124.

This PR reverts #12889 (except tests) and adds the fix to call tqdm.initial = 0, which prevents from tqdm incorrectly computing the estimates for ETA and iterations/s. All tests added in #12889 remain as they are not affected by reverting this.

Since this is a visual bug, and the tqdm produces these estimates at runtime, I don't really know how to test this. Suggestions by reviewers are welcome!

Repro example used to debug:

import os
import shutil
from time import sleep

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

from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks import ModelCheckpoint


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):
        sleep(.1)
        return self.layer(x)

    def training_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("train_loss", loss)
        print()
        return {"loss": loss}

    def validation_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("val_loss", loss)
        print()

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


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

    if os.path.exists("lightning_logs"):
        shutil.rmtree("lightning_logs")

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        max_epochs=1,
        enable_model_summary=False,
        enable_progress_bar=True,
        callbacks=ModelCheckpoint(monitor="train_loss", save_top_k=-1, every_n_train_steps=1),
    )
    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=train_data)

    trainer = Trainer(
        default_root_dir=os.getcwd(),
        max_epochs=3,
        enable_model_summary=False,
        enable_progress_bar=True,
        callbacks=ModelCheckpoint(monitor="train_loss", save_top_k=-1, every_n_train_steps=1),
    )
    trainer.fit(
        model, train_dataloaders=train_data, ckpt_path="lightning_logs/version_0/checkpoints/epoch=0-step=3.ckpt"
    )


if __name__ == "__main__":
    run()

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

No.

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Did you have fun?

I made sure I had fun coding 🙃

cc @Borda @tchaton @rohitgr7 @awaelchli @akihironitta

[codecov]

Codecov Report

Merging #13962 (67b62e6) into master (af07e75) will decrease coverage by 9%.
The diff coverage is 100%.

@@            Coverage Diff            @@
##           master   #13962     +/-   ##
=========================================
- Coverage      86%      76%     -9%     
=========================================
  Files         341      341             
  Lines       26563    26565      +2     
=========================================
- Hits        22732    20221   -2511     
- Misses       3831     6344   +2513