Since my method is an Autoregressive algorithm It is making a huge gradient tape, I am trying to do something like this
for i in range(len(maxtrix.shape)): output = torch.utils.checkpoint.checkpoint(NNModel(matrix[i])) loss = -output.mean()
where NNModel is a torch.nn.Module.
It works fine on single GPU but on DDP it throws this error
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 30 with name module.model.decoder.decoder_network.layers.1.weight has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration.
I am running it with
Any workaround for this?