Register batch complete progress before batch_end hooks

[lantiga]

What does this PR do?

Fixes #14579

The following code

import torch
from torch.utils.data import DataLoader, Dataset
 
import lightning as L
from lightning.pytorch.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(L.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()
       return {"loss": loss}
 
   def configure_optimizers(self):
       return torch.optim.SGD(self.layer.parameters(), lr=0.1)

 
train_data = DataLoader(RandomDataset(32, 24), batch_size=2)
 
model = BoringModel()

trainer = L.Trainer(
   default_root_dir=".",
   max_steps=10,
   enable_model_summary=False,
   accelerator="cpu",
   callbacks=ModelCheckpoint(dirpath="./lightning_logs/checkpoints", save_last=True, save_top_k=-1, every_n_train_steps=10),
)
trainer.fit(model, train_data)

trainer = L.Trainer(
   default_root_dir=".",
   max_steps=20,
   enable_model_summary=False,
   accelerator="cpu",
   callbacks=ModelCheckpoint(dirpath="./lightning_logs/checkpoints", save_last=True, save_top_k=-1, every_n_train_steps=10),
)
trainer.fit(model, train_data, ckpt_path='last')

will produce skewed progress information in the checkpoints, compared to the case where there is no restart.

This is due to the fact that when ModelCheckpoint is triggered on on_train_batch_end, it won't see batch_progress.total.completed updated to the latest batch that was processed, because progress is updated right after the hook is called.

However, upon restart, there won't be any opportunity to register the actual completion of the batch, causing a skew that is proportional to the number of restarts. This impacts the time at which validation is called, which itself becomes dependent from restarts.

This PR addresses this issue by first updating batch progress and then calling batch_end hooks.

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• Did you make sure to update the documentation with your changes? (if necessary)
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• Did you verify new and existing tests pass locally with your changes?
• Did you list all the breaking changes introduced by this pull request?
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---

📚 Documentation preview 📚: https://pytorch-lightning--20376.org.readthedocs.build/en/20376/

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Check ID	Status
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pl-cpu (windows-2022, lightning, 3.10, 2.1)	failure	❌
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