ModelCheckpointCallback is triggered by mistake after every validation stage when mannual optimization

[silverbulletmdc]

Bug description

I set the every_n_epochs param of ModelCheckpoint to 1 and val_check_interval of trainer to 200. The total iter of a batch is 1000. It should not save checkpoint files after the val_check. But it does.

What version are you seeing the problem on?

v2.4

How to reproduce the bug

No response

Error messages and logs

# Error messages and logs here please

Environment

Current environment

#- PyTorch Lightning Version (e.g., 2.4.0):
#- PyTorch Version (e.g., 2.4):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):

More info

No response

cc @tchaton @justusschock @awaelchli @Borda

[lantiga]

Thanks for reporting this. Is that the case with the latest master as well?

[lantiga]

Ok, I verified and can reproduce

import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader

import lightning as L
from lightning.pytorch.demos import Transformer, WikiText2
from lightning.pytorch.callbacks import ModelCheckpoint


class LanguageModel(L.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.vocab_size = vocab_size
        self.model = Transformer(
            vocab_size=self.vocab_size,
            nlayers=2,
            nhid=4096,
            ninp=1024,
            nhead=8,
        )

    def training_step(self, batch):
        input, target = batch
        output = self.model(input, target)
        loss = F.nll_loss(output, target.view(-1))
        self.log("train_loss", loss, prog_bar=True)
        return loss

    def validation_step(self, batch):
        input, target = batch
        output = self.model(input, target)
        loss = F.nll_loss(output, target.view(-1))
        self.log("val_loss", loss, prog_bar=True)

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=1e-4)



def train():
    L.seed_everything(42)

    dataset = WikiText2()
    train_dataloader = DataLoader(dataset, num_workers=8, batch_size=1)
    val_dataloader = DataLoader(dataset, num_workers=8, batch_size=1)

    model = LanguageModel(vocab_size=dataset.vocab_size)

    model_checkpoint = ModelCheckpoint(save_top_k=-1, every_n_epochs=2, save_last=False)

    trainer = L.Trainer(
        max_steps=100,
        precision="bf16-true",
        limit_train_batches=10,
        limit_val_batches=2,
        callbacks=model_checkpoint,
        val_check_interval=5
    )
    trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)


if __name__ == "__main__":
    torch.set_float32_matmul_precision("high")
    train()

[lantiga]

BTW if you change

model_checkpoint = ModelCheckpoint(save_top_k=-1, every_n_epochs=2, save_last=False)

to

model_checkpoint = ModelCheckpoint(save_top_k=-1, every_n_epochs=2, save_on_train_epoch_end=True, save_last=False)

then you'll get checkpoints saved at the right interval.

However if you keep the interval as in the above snippet, you indeed get something like this:

'epoch=1-step=15.ckpt'
'epoch=1-step=20.ckpt'
'epoch=3-step=35.ckpt'
'epoch=3-step=40.ckpt'
'epoch=5-step=55.ckpt'
'epoch=5-step=60.ckpt'
'epoch=7-step=75.ckpt'
'epoch=7-step=80.ckpt'
'epoch=9-step=95.ckpt'
'epoch=9-step=100.ckpt'

i.e. you get a checkpoint at all validation steps, in the training epoch when you're supposed to save and not the other. Which is of course consistent with the code in ModelCheckpoint but it's indeed a bit surprising.

[lantiga]

So the resolution for this is use

ModelCheckpoint(..., save_on_train_epoch_end=True)

to avoid saving on validation_end.

In the future we could introduce a save_on_validation_end=True argument to make things more explicit. It would clarify in this case, wdyt?

[silverbulletmdc]

So the resolution for this is use

ModelCheckpoint(..., save_on_train_epoch_end=True)

to avoid saving on validation_end.

In the future we could introduce a save_on_validation_end=True argument to make things more explicit. It would clarify in this case, wdyt?

Thanks for your reply. I'll try it.