how to save and load model, optimizer and scheduler's state dictionary?

[samarth-b]

How do I save and load the model, optimizer and scheduler state dictionarys that has gone through accelerator.prepare()?

for model

I used the unwrap function as described in the documentation

accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.model_path, 
                            save_function=accelerator.save, 
                            state_dict=accelerator.get_state_dict(model))

however, I get the following error when loading the model
model = MT5ForConditionalGeneration.from_pretrained(args.model_path, config=config)

    model, optimizer, training_loader, dev_loader = accelerator.prepare(
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/accelerate/accelerator.py", line 269, in prepare
    result = tuple(self._prepare_one(obj) for obj in args)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/accelerate/accelerator.py", line 269, in <genexpr>
    result = tuple(self._prepare_one(obj) for obj in args)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/accelerate/accelerator.py", line 227, in _prepare_one
    return self.prepare_model(obj)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/accelerate/accelerator.py", line 285, in prepare_model
    model = model.to(self.device)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 673, in to
    return self._apply(convert)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 387, in _apply
    module._apply(fn)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 409, in _apply
    param_applied = fn(param)
  File "/dccstor/cssblr/samarth/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 671, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable

For optimizer and scheduler

currently using torch.save(optimizer.state_dict(), /exp1/file.opt) for save gives the error RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable when loading using optimizer.load_state_dict(torch.load('exp1/file.opt'))

Does accelerator.unwrap( work the same way as for a model?

accelerator.wait_for_everyone()
unwrapped_optmizer = accelerator.unwrap_model(optmizer)
accelerator.save(unwrapped_optmizer.state_dict(), filename)

Using torch.save(scheduler.state_dict(), /exp1/sch)and loading withscheduler.load_state_dict(torch.load('path')` is working.

EDITS: I updated the original issue with more details and exact error messages.

[sgugger]

You should use accelerator.save everywhere and not torch.save (though I must say I have never seen that particular error).
For reloading, you should be able to reload a state dict in the unwrapped model or the optimizer. If you do

model = MT5ForConditionalGeneration.from_pretrained(args.model_path, config=config)

You create a brand new model, so you should pass it to the prepare method.

Note that adding checkpointing utility in Accelerate is on the roadmap, to make all of this easier.

[samarth-b]

thanks, I was able to load the model with torch.save('filename', map_location='cpu') so it looks like unwrap_model needs to remove any location GPU information.

[seanbenhur]

Note that adding checkpointing utility in Accelerate is on the roadmap, to make all of this easier.

Is this feature, currently available?

[sgugger]

It's under development on #255, we're hoping to have it merge next week.

[muellerzr]

Closed with #255! 🎉