[wip] Make RichProgressBar use auto_refresh=True

[akihironitta]

What does this PR do?

Tries to fix #13937 by partially reverting #10497 to revive auto_refresh=True.

code for benchmark

import torch
from torch.utils.data import DataLoader, Dataset
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks import RichProgressBar

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

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

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

def main():
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    model = BoringModel()
    trainer = Trainer(
        max_epochs=100,
        enable_model_summary=False,
        enable_checkpointing=False,
        logger=False,
        benchmark=False,  # True by default in 1.6.{0-3}.
        callbacks=RichProgressBar(),
    )
    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)

if __name__ == "__main__":
    main()

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[akihironitta]

(Still wip but) Following Textualize/rich#2432, it now uses auto_refresh=True in this PR, and I see a significant improvement locally:

time: 4.771114709   # 4ca7033693f0bef01ac724ca83e4c6c4c9550b42 (bugfix/rich-auto-refresh)
time: 12.920467208  # b84c03f3a6c5327814c6aaab5634e5b5d46919d8 (master)

code

from time import monotonic
import torch
from torch.utils.data import DataLoader, Dataset
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks import RichProgressBar

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 validation_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("valid_loss", loss)

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
        return [optimizer], [lr_scheduler]

def main():
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    model = BoringModel()
    trainer = Trainer(
        max_epochs=100,
        enable_model_summary=False,
        enable_checkpointing=False,
        logger=False,
        benchmark=False,  # True by default in 1.6.{0-3}.
        callbacks=RichProgressBar(),
    )
    t0 = monotonic()
    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
    print("time:", monotonic() - t0)

if __name__ == "__main__":
    main()

[akihironitta]

⚠️ Not ready ⚠️ Marked it as ready just to trigger CI.

[justusschock]

@akihironitta How is it going here? Just a friendly reminder :)

[PKizzle]

May I ask why this PR has been closed?