Making train_parallel.py work on a single machine (non-distributed) #29
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| @@ -274,12 +288,15 @@ def main(args): | |||
| "args": args, | |||
| } # save full config. | |||
| ema_model.save(vision_model_file, other_items=other_items) | |||
| diffusion_model.model.module.vision_model.save( | |||
| getattr(diffusion_model.model, "module", diffusion_model.model).vision_model.save( | |||
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Why do we need getattr here, does the diffusion_model.model sometimes not have a module? Or is this connected to the torch change?
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It seems that diffusion_model.model only has module when it's in the nn.parallel.DistributedDataParallel container, so this getattr accounts for when model = diffusion_model.model.
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If there's a better way to account for this (e.g. just adding the module attribute to the model instance if it's missing), please let me know and I can make the change!
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Small tweaks to train_parallel.py (and related changes to distributed.py and generate_batch.py) so that it works on a single machine. Mainly, it now only uses the nn.parallel.DistributedDataParallel model container if in a distributed environment.