DiT
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facebookresearch
Resume training from a checkpoint
Thanks for your great work. I implement the resuming choice for training. It can be used as following.
For example,
torchrun --nnodes=1 --nproc_per_node=8 train.py --model DiT-XL/2 --data-path /path/to/imagenet/train --resume results/000/checkpoints/0100000.pt
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Thank you very much for your resume. I encountered an error here. What is ‘_ddp_dict,’? Thank you！ line 203 in train.py NameError: name '_ddp_dict' is not defined
Hi,
It should be as following.
def _ddp_dict(_dict):
new_dict = {}
for k in _dict:
new_dict['module.' + k] = _dict[k]
return new_dict
This looks really awesome! @yukang2017 I was doing something similar, but when I resume from a checkpoint, it seems the loss is not exactly going down from the point where it stopped, do you observe a similar phenomenon?
@yukang2017 this is great and a much needed feature to be added. I tried your modifications to resume from a checkpoint. The loss in the beginning was around 0.21 and after 1M iterations was about 0.14. But upon restarting from the checkpoint with your modifications, the loss goes back to the starting value (0.21).
I believe there could be bug that resets the loss value ? I also check that you save the optimizer state, so not sure what this is about.
It would be great if you could please investigate.
@yukang2017 I observe the same exact issue as you mentioned. Loss goes back up. I wonder if this may be due to the EMA weights ?
@achen46 I am also having a similar question whether this is ude to the EMA weights, but I thought EMA weights has been stored right?
@NathanYanJing I believe so as the saved model is too large ~ 9G. But it could also be the fact that we overwrite them again and hence loss going back to the beginning value.
It would be great to know @yukang2017 opinion
@achen46 I’ve encountered the exact same issue you described earlier, and as a result, I’ve created a new pull request #36 . I’ve tested it on my end, and it works for me. Hopefully, it will be helpful to you as well.
@achen46 I’ve encountered the exact same issue you described earlier, and as a result, I’ve created a new pull request #36 . I’ve tested it on my end, and it works for me. Hopefully, it will be helpful to you as well.
Hi @Littleor thanks a lot. I will check it out and verify.