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您好同学!我在使用ith_cp=True, # using checkpoint to save GPU memory发生
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 410 with name img_backbone.layer4.2.conv3.weight has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration. ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 397718) of binary: /home/bailiangliang/anaconda3/envs/surroundocc/bin/python
请问我该如何修改代码?
The text was updated successfully, but these errors were encountered:
您好同学!我在使用ith_cp=True, # using checkpoint to save GPU memory发生
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 multiplecheckpoint
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 410 with name img_backbone.layer4.2.conv3.weight has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration. ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 397718) of binary: /home/bailiangliang/anaconda3/envs/surroundocc/bin/python请问我该如何修改代码?
The text was updated successfully, but these errors were encountered: