ImageNet Classification Fine-tuning

[LITTLEKKKK]

Sorry to bother you. I met a mistake during fine-tuning on EVA-01. 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: 2 3

[LITTLEKKKK]

I added find_unused_parameters=True to model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True). And I found the blocks.39.mlp.fc2.weigh has been marked as ready twice.

RuntimeError: Expected to mark a variable ready only once. This error is caused by one of the following reasons: 1) Use of a module parameter outside the forward function. Please make sure model parameters are not shared across multiple concurrent forward-backward passes. or try to use _set_static_graph() as a workaround if this module graph does not change during training loop.2) Reused parameters in multiple reentrant backward passes. For example, if you use multiple checkpoint functions to wrap the same part of your model, it would result in the same set of parameters been used by different reentrant backward passes multiple times, and hence marking a variable ready multiple times. DDP does not support such use cases in default. You can try to use _set_static_graph() as a workaround if your module graph does not change over iterations. Parameter at index 522 with name blocks.39.mlp.fc2.weight has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration. torch.autograd.backward(outputs_with_grad, args_with_grad)

[tb2-sy]

I added find_unused_parameters=True to model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True). And I found the blocks.39.mlp.fc2.weigh has been marked as ready twice.

RuntimeError: Expected to mark a variable ready only once. This error is caused by one of the following reasons: 1) Use of a module parameter outside the forward function. Please make sure model parameters are not shared across multiple concurrent forward-backward passes. or try to use _set_static_graph() as a workaround if this module graph does not change during training loop.2) Reused parameters in multiple reentrant backward passes. For example, if you use multiple checkpoint functions to wrap the same part of your model, it would result in the same set of parameters been used by different reentrant backward passes multiple times, and hence marking a variable ready multiple times. DDP does not support such use cases in default. You can try to use _set_static_graph() as a workaround if your module graph does not change over iterations. Parameter at index 522 with name blocks.39.mlp.fc2.weight has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration. torch.autograd.backward(outputs_with_grad, args_with_grad)

I have encountered the same problem, did you solve it, thank you!

[wytcsuch]

same erro...

[wytcsuch]

It seems that set "use_checkpoint" to False can solve the problem