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View Code? Open in Web Editor NEWPyTorch – SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models.
License: MIT License
PyTorch – SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models.
License: MIT License
Hi, thanks for you implementation.
But i found this code seem imcompatible with DDP training, and errors would happen in line
# Get initial embeddings
embed = self.model.roberta.embeddings(input_ids)
in your https://github.com/archinetai/smart-pytorch#roberta-classification-example.
Hi,
Thank you for the code.
Thanks.
Hello,
Thank you for providing the implementation but I seem to be running into the following issue
RuntimeError: One of the differentiated Tensors appears not to have been used in the graph. Set allow_unused=True if this is the desired behavior.
This happens at the following step:
loss = self.loss_fn(state_perturbed, state.detach())
# Compute noise gradient ∂loss/∂noise
noise_gradient, = torch.autograd.grad(loss, noise)
Any guidance on why this could be happening?
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