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mayuelala avatar mayuelala commented on July 27, 2024

Hi,thanks for your attention.
maybe you could fork our code for your multi-gpu training.

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Trainingzy avatar Trainingzy commented on July 27, 2024

Thanks for your reply. I tried your code on my dataset and train loop. I encounter the same problem as using Tune-A-Video codebase multi-gpu training:

RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by making sure all `forward` function outputs participate in calculating loss. 
If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's
 `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).                                                                                                                                        
Parameter indices which did not receive grad for rank 1: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
 In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular paramet
ers did not receive gradient on this rank as part of this error 

I really appreciate it if you could tell me how do u fix the multi-gpu training problem in Tune-A-Video.

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mayuelala avatar mayuelala commented on July 27, 2024

maybe try to set gradient_checkpointing: False in here

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Trainingzy avatar Trainingzy commented on July 27, 2024

Oh, it works now! Thanks!

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