Out-of-memory when loading the checkpoint w/o setting device id
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Description
Hi, thanks for sharing this framework! Really easy to use.
Still, I met a problem when I tied to add checkpointing logic. I think the core problem is whether we should specify the device_id when loading checkpoints to cuda memory (in distributed setting).
In the official document we only need to do something like torch.load(ckpt_f)
While in this ImageNet example, it is
torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.gpu))
So I am wondering why this is necessary even if I called torch.cuda.set_device(args.gpu) before.
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Research direction
Start with the checkpointing section of the AMP documentation and examples/imagenet/main_amp.py around line 183. Reproduce the distributed checkpoint load using torch.load with and without map_location after torch.cuda.set_device(args.gpu), then trace the resulting device placement and memory use. Done should mean the cause and a supported loading behavior are documented or covered by a focused fix and test.
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Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100