Provide a Default Parameter for Fit's Checkpoint Restore Path

[blisc]

Proposed refactor

Do 1 of 2 options:

1. Do not deprecate resume_from_checkpoint from Trainer's initialization as recommended in [checkpoint] Resolve 2 different checkpoint loading paths across fit vs validate/test/predict #9405
2. Provide a public facing property that acts as a default in the case when ckpt_path is passed as None to trainer.fit. This property should be accessible after trainer initialization

Motivation

Our current design pattern is to initialization the Trainer object, and then inject our own logic into the Trainer class, and finally calling trainer.fit(model) without adding any additional arguments to fit(). Moving this argument from trainer's init to fit() will require us to rewrite all of our training scripts.

Pitch

See Proposed refactor

[ananthsub]

Hi @blisc I originally proposed the deprecation in #9405 .

These are my opinions:

1. The Lightning framework should offer a single convention that works across the different trainer functions. Offering resume_from_checkpoint for fit in the trainer constructor vs ckpt_path as arguments to validate / test / predict was a broken experience.

That means either:

• Make ckpt_path an argument to each of the trainer functions
• Or add fit_ckpt_path, validate_ckpt_path, test_ckpt_path and predict_ckpt_path in the trainer constructor.
  While the latter makes things more configurable, why add them in the constructor vs the function the arguments are prefixed with? Where do you draw the line?

then inject our own logic into the Trainer class

Could you describe what this does and why it blocks calling trainer.fit(model, ckpt_path="...") ?

[blisc]

Could you describe what this does and why it blocks calling trainer.fit(model, ckpt_path="...")

Consider an average lightning training script:

MyModel = LightningModules(**model_cfg)
trainer = Trainer(**trainer_cfg)
# Additional logic here to add logging and checkpoint that is common to all scripts
myfunction(trainer)  # I want some ability to set a default checkpoint for fit
trainer.fit(model)  # Note: We define our dataloaders in the model itself

Since the training script is so similar, we just copy it for every model. Requiring a change in fit() causes us to change the script for every model. This gets unfeasible as the number of models grows.
I want some ability to change trainer parameters inside of myfunction() so we can just change the single function as opposed to all the train_{model}.py files.

[ananthsub]

@blisc - it seems like the main issue is then around myfunction and adding defaults that it might not make sense for the Lightning trainer to support.

#9741 is the issue we filed to allow users the ability to register implementations, which the lightning trainer can then look up & add automatically for callbacks/loggers/profiler/plugins. Would that address your concern?

However, In your example, I don't see why you couldn't look up the checkpoint path for fit from the trainer_cfg and pass it to trainer.fit(model, ckpt_path)

[blisc]

#9741 is the issue we filed to allow users the ability to register implementations, which the lightning trainer can then look up & add automatically for callbacks/loggers/profiler/plugins. Would that address your concern?

I'm not asking for a custom plugin. I'm asking for lightning to continue supporting what it has in the past

However, In your example, I don't see why you couldn't look up the checkpoint path for fit from the trainer_cfg and pass it to trainer.fit(model, ckpt_path)

I'm trying to avoid changing anything in train_{model}.py files.

---

Your main goal was to unify the lightning experience and you have already,

All I am asking for is a parameter that is part of lightning trainer that can be used as the default if the relevant arguments are not passed to fit, validate, test, predict. You already have it for validate, test, predict. Can you just add fit as well, then everything will be unified!

[ananthsub]

All I am asking for is a parameter that is part of lightning trainer that can be used as the default if the relevant arguments are not passed to fit, validate, test, predict. You already have it for validate, test, predict. Can you just add fit as well, then everything will be unified!

Could you share a code pointer for what you're looking for just so we're clear on the request?

[ananthsub]

Discussed with @blisc - the request is to have a self.fit_ckpt_path defined as an attribute on the Trainer, similar to the ones here: https://github.com/PyTorchLightning/pytorch-lightning/blob/247f5aacc2fc96ab1df14d7176838dc65757a786/pytorch_lightning/trainer/trainer.py#L474-L476

Is this supported behavior?

trainer.validated_ckpt_path = "path"
trainer.validate(model, dataloaders)

vs

trainer.validate(model, dataloaders, ckpt_path="path")

this seems more like a quirk of attributes on the Trainer that were inadvertently made public instead of private. @awaelchli @carmocca other checkpointing API experts, do you know? It's certainly not documented anywhere, and offering only the 2nd option feels more natural to support.

[SeanNaren]

trainer.validated_ckpt_path = "path"
trainer.validate(model, dataloaders)

Yikes! Imo that's a strange compromise. If we're now ok advertising two different ways to set the variable for our users, I'd rather us just introduce Trainer(fit_ckpt_path=...).

[awaelchli]

this seems more like a quirk of attributes on the Trainer that were inadvertently made public instead of private. @awaelchli @carmocca other checkpointing API experts, do you know?

These attributes were basically there since the beginning. Their purpose originally was to serve as read-only to access programmatically the path that was loaded, i.e., when setting "best" as the checkpoint path. And it also served as a way to test the Trainer (no longer done this way).

trainer.validated_ckpt_path = "path"
trainer.validate(model, dataloaders)

This is not supposed to work, it is an unintended behavior and one should not rely on it, not today.

[blisc]

Ok, it seems like you are leaning towards removing those attributes. Then can I suggest the following compromise:

• Have a single argument in Trainer's initialization
  • Whether this is still resume_from_checkpoint or a new argument is up to you. For the purpose of this discussion, I'll just call it default_ckpt_path
  • It will support all possible values of ckpt_path as passed to fit, validate, etc. That is it will support either a path to a checkpoint or "best"
  • This will just set some trainer property: trainer.default_ckpt_path = default_ckpt_path
• On calls to fit, validate, etc.
  • Run something like: if ckpt_path is None; chpt_path = trainer.default_ckpt_path

There will only be two ways to set ckpt_path:

1. Passing it as ckpt_path to the relevant function
2. Passing a default_ckpt_path and using this property

This does not block any further refactor/unification that @ananthsub still has left to do.