Smoothing in tqdm progress bar has no effect

[heth27]

Bug description

The option smoothing when creating progress bars in TQDMProgressBar has no effect in the default implementation, as
_update_n only calls bar.refresh() and not the update method of the progress bar. Thus only the global average is taken, as the update method of the tqdm class is responsible for calculating moving averages.
Either the update method of the progress bar could be used or it should be added to the documentation if smoothing having no effect is the desired behavior (overriding a default that has no effect is a bit misleading)

What version are you seeing the problem on?

master

How to reproduce the bug

import time

import lightning.pytorch as pl
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader, Sampler

from src.main.ml.data.data_augmentation.helpers.random_numbers import create_rng_from_string
import sys
from typing import Any

import lightning.pytorch as pl
from lightning.pytorch.callbacks import TQDMProgressBar
from lightning.pytorch.callbacks.progress.tqdm_progress import Tqdm
from lightning.pytorch.utilities.types import STEP_OUTPUT
from typing_extensions import override


class LitProgressBar(TQDMProgressBar):
    """
    different smoothing factor than default lightning TQDMProgressBar, where smoothing=0 (average),
     instead of smoothing=1 (current speed) is taken

     See also:
     https://tqdm.github.io/docs/tqdm/
    """

    def init_train_tqdm(self) -> Tqdm:
        """Override this to customize the tqdm bar for training."""
        return Tqdm(
            desc=self.train_description,
            position=(2 * self.process_position),
            disable=self.is_disabled,
            leave=True,
            dynamic_ncols=True,
            file=sys.stdout,
            smoothing=1.0,
            bar_format=self.BAR_FORMAT,
        )

    # default method

    # @override
    # def on_train_batch_end(
    #     self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int
    # ) -> None:
    #     n = batch_idx + 1
    #     if self._should_update(n, self.train_progress_bar.total):
    #         _update_n(self.train_progress_bar, n)
    #         self.train_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))


   # my own method that uses smoothing by using the update method of progress bar
    @override
    def on_train_batch_end(
            self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any,
            batch_idx: int
    ) -> None:
        n = batch_idx + 1
        if self._should_update(n, self.train_progress_bar.total):
            self.train_progress_bar.update(self.refresh_rate)
            self.train_progress_bar.set_postfix(self.get_metrics(trainer, pl_module))


class TestModule(nn.Module):
    def __init__(self, in_dim=512, out_dim=16):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.simple_layer = nn.Linear(self.in_dim, self.out_dim, bias=True)

    def forward(self, input):
        return self.simple_layer(input)


class TestBatchSampler(Sampler):
    def __init__(self, step=0):
        super().__init__()
        self.step = step

    def __len__(self) -> int:
        return 1e100
        # return len(self.train_allfiles)

    def __iter__(self):  # -> Iterator[int]:
        return self

    def __next__(self):  # -> Iterator[int]:
        return_value = self.step
        self.step += 1
        return [return_value]


class TestDataset(Dataset):
    def __init__(self, in_dim):
        super().__init__()
        self.in_dim = in_dim
        self.total_len = 512

    def __len__(self):
        return 1

    def __getitem__(self, idx):
        rng = create_rng_from_string(
            str(idx) + "_"
            + "random_choice_sampler")
        return torch.tensor(rng.random(self.in_dim), dtype=torch.float32)


class TestDataModule(pl.LightningDataModule):
    def __init__(self, start_step=0):
        super().__init__()
        self.in_dim = 512
        self.val_batch_size = 1
        self.start_step = start_step

    def train_dataloader(self):
        train_ds = TestDataset(self.in_dim)
        train_dl = DataLoader(train_ds, batch_sampler=TestBatchSampler(step=self.start_step), num_workers=4,
                              shuffle=False)
        return train_dl


class TestLitModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.test_module_obj = TestModule(in_dim=512, out_dim=16)
        self.automatic_optimization = False

    def training_step(self, batch, batch_idx):
        if batch_idx == 0:
            time.sleep(5)

        time.sleep(0.5)

        optimizer = self.optimizers()

        output = self.test_module_obj(batch)

        loss = output.sum()

        self.manual_backward(loss)

        optimizer.step()

    def configure_optimizers(self):
        optimizer = torch.optim.AdamW(
            self.test_module_obj.parameters()
        )
        return optimizer


if __name__ == '__main__':
    test_data_loader = TestDataModule()
    test_lit_model = TestLitModel()

    bar = LitProgressBar(refresh_rate=5)
    trainer = pl.Trainer(
        log_every_n_steps=1,
        callbacks=[bar],
        max_epochs=-1,
        max_steps=400000,
    )

    trainer.fit(test_lit_model,
                datamodule=test_data_loader)

### Error messages and logs

Error messages and logs here please

### Environment

<details>
  <summary>Current environment</summary>

#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(conda, pip, source):
#- Running environment of LightningApp (e.g. local, cloud):

</details>


### More info

_No response_

cc @borda

[awaelchli]

as _update_n only calls bar.refresh() and not the update method of the progress bar

This change is needed to give us exact control in all use cases to enable exact updates. The progress bar is deeply tied to the loops so we need that precise control. So we can't remove the _update_n change. But you can definitely override everything in TQDMProgressBar you wish and make it your own if that smoothing option is important.

or it should be added to the documentation if smoothing having no effect is the desired behavior (overriding a default that has no effect is a bit misleading)

I'm fine with adding a note to the TQDMProgressBar.init_*_tqdm methods documentation 👍