使用lora方式训练时，出现以下错误
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 59 has been marked as ready twice. This means that multiple
autograd engine hooks have fired for this particular parameter during this
iteration. You can set the environment variable TORCH_DISTRIBUTED_DEBUG to
either INFO or DETAIL to print parameter names for further debugging.
zhangzhuoran997997
commented
1 year ago
使用lora方式训练时，出现以下错误 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
forwardfunction. 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 multiplecheckpointfunctions 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 59 has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration. You can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print parameter names for further debugging.