Can't cast Trainer automatically generated args to their required types

[alexeykarnachev]

❓ Questions and Help

What is your question?

I'm not sure, that this is a bug, so I put it like a question.
The problem is: if I want to add the Trainer arguments to my custom ArgumentParser object, I call the add_argparse_args Trainer classmethod. But this method doesn't cast the Trainer arguments to their required types.
It forces me to cast the arguments by myself. Like so:

trainer_args.update(
        {
            'accumulate_grad_batches': int(trainer_args['accumulate_grad_batches']),
            'train_percent_check': float(trainer_args['train_percent_check']),
            'val_percent_check': float(trainer_args['val_percent_check']),
            'val_check_interval': int(trainer_args['val_check_interval']),
            'track_grad_norm': int(trainer_args['track_grad_norm']),
            'max_epochs': int(trainer_args['max_epochs']),
            'precision': int(trainer_args['precision']),
            'gradient_clip_val': float(trainer_args['gradient_clip_val']),
        }
)

And after that, I can pass updated arguments to the Trainer:

trainer = pytorch_lightning.Trainer(
        **trainer_args
)

And I can't find a central place, where the Trainer handles an automatically generated arguments (their types).

What have you tried?

I've tried to pass arguments to the trainer without handling their types. For instance, if i'll not cast accumulate_grad_batches to the integer type, the exception will be raised:

TypeError: Gradient accumulation supports only int and dict types

What's your environment?

• OS: [Linux]
• Packaging [pip]
• Version [0.7.1]

[Borda]

@alexeykarnachev could you pls give a complete example, I am missing which is the ArgumentParser... here it seems like you want to update the trainer_args by itself

trainer_args.update({'accumulate_grad_batches': int(trainer_args['accumulate_grad_batches']), ...)

if you need the default values, pls use default_attributes

[alexeykarnachev]

Yes, sorry for the insufficient details from my side.

Here is a gist:
https://gist.github.com/alexeykarnachev/fd010b797d92873905780c856e9334d5

It's not runnable script, because I've simplified it a lot. But it contains the problem i've described.

If I'll execute this script with any Trainer argument, for example:

python script.py --my_custom_arg_1=1 --my_custom_arg_n=2 --accumulate_grad_batches=1

It will fail without the types casting, because accumulate_grad_batches will be interpreted as a string, and Trainer will fail.

[Borda]

if do understand your use-case you want to have some parameters to be passed from CMD and/but some Trainer arguments to be fixed - forced to have a certain value, right?

fixed_args = {
            'logger': tb_logger_callback,
            'checkpoint_callback': model_checkpoint_callback,
            'show_progress_bar': True,
            'progress_bar_refresh_rate': 1,
            'row_log_interval': 1,
}
trainer_args = vars(args)
trainer_args.update(fixed_args)

but in the case of accumulate_grad_batches (and some others) we shall do some casting eg with eval
Woudl you mind sending a PR to fix Trainer.from_argparse_args(...) and/or Trainer.add_argparse_args(...) containing also type?

[alexeykarnachev]

Yes, I'll do it.

But do you have any suggestions of how can it be performed ?
I see it like this:
in the Trainer.add_argparse_args classmethod there is a loop:

        for arg in trainer_default_params:
            parser.add_argument(
                f'--{arg}',
                default=trainer_default_params[arg],
                dest=arg,
                help='autogenerated by pl.Trainer'
            )

And it's possible to pass a type argument to the parser.add_argument, but this "type" first needs to be obtained somehow.
I suppose, that it can be done via the signature inspection in the Trainer.default_attributes classmethod.