Checkpoint migration for ModelCheckpoint state-key changes

[awaelchli]

What does this PR do?

A breaking change was introduced in #15300 regarding the state loading for the model checkpoint callback. Checkpoints that have the the state saved with save_on_train_epoch_end=True|False can still be loaded, but the ModelCheckpoint won't load its state correctly, since the code and what's saved in the checkpoint do not match.

We can fix this issue by dropping the save_on_train_epoch_end from the state key. This way, all old states from ModelCheckpoint can be loaded successfully without ambiguity in what save_on_train_epoch_end should be set to.
An extremely rare edge case in which a migration is not possible is also handled with a user warning.

Full repro script:

import os

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):
        return self.layer(x)

    def on_train_start(self) -> None:
        print("The callback states at the beginning of training are")
        for c in self.trainer.callbacks:
            print(c.state_key, c.state_dict())

    def on_train_end(self) -> None:
        print("The callback states at the end of training are")
        for c in self.trainer.callbacks:
            print(c.state_key, c.state_dict())

    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_loss1", loss)
        self.log("valid_loss2", loss)

    def test_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("test_loss", loss)

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


def run():
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)

    checkpoint1 = ModelCheckpoint(monitor="valid_loss1")  # in v1.7 -> True
    checkpoint2 = ModelCheckpoint(monitor="valid_loss2", save_on_train_epoch_end=False)  # in v1.7 -> False
    checkpoint3 = ModelCheckpoint(monitor="train_loss", save_on_train_epoch_end=True)  # in v1.7 -> True

    name = "check/verify1-7-7.ckpt"
    resume = False  # train on 1.7.7, switch to 1.8.0 and flip resume to True
    
    if not resume:

        model = BoringModel()
        trainer = Trainer(
            default_root_dir=os.getcwd(),
            limit_train_batches=1,
            limit_val_batches=1,
            limit_test_batches=1,
            num_sanity_val_steps=0,
            max_epochs=1,
            callbacks=[checkpoint1, checkpoint2, checkpoint3],
            enable_model_summary=False,
        )
        trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
        trainer.save_checkpoint(name)
    else:
        model = BoringModel()
        trainer = Trainer(
            default_root_dir=os.getcwd(),
            limit_train_batches=1,
            limit_val_batches=1,
            limit_test_batches=1,
            num_sanity_val_steps=0,
            max_epochs=2,
            enable_model_summary=False,
            callbacks=[checkpoint1, checkpoint2, checkpoint3],
        )
        trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data, ckpt_path=name)


if __name__ == "__main__":
    run()

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Did you have fun?

I made sure I had fun coding 🙃

cc @Borda @awaelchli @justusschock

[carmocca]

Great!