Got an Error while retraining model using ./train.sh

[christoph-meyer-horsch]

Hi,
I am trying to retrain the given model with a new dataset for my thesis. Preprocessing worked fine but now I get the following error when trying to run train.sh:

neg_target = target.new_tensor(target).masked_fill_(target_label, self.padding_idx)
RuntimeError: The expanded size of the tensor (384) must match the existing size (832) at non-singleton dimension 0. Target sizes: [384]. Tensor sizes: [832]

I didn't change anything besides the pretrained model path in train.sh.

I previously fixed this error

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [128, 1, 1536]], which is output 0 of AddBackward0, is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

by changing q *= self.scaling to q = q * self.scaling in line 109 of the multihead_attention.py of fairseq.

Thank you.

[Hexa4C]

Hi,
I am trying to retrain the given model with a new dataset for my thesis. Preprocessing worked fine but now I get the following error when trying to run train.sh:

neg_target = target.new_tensor(target).masked_fill_(target_label, self.padding_idx)
RuntimeError: The expanded size of the tensor (384) must match the existing size (832) at non-singleton dimension 0. Target sizes: [384]. Tensor sizes: [832]

I didn't change anything besides the pretrained model path in train.sh.

I previously fixed this error

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [128, 1, 1536]], which is output 0 of AddBackward0, is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

by changing q *= self.scaling to q = q * self.scaling in line 109 of the multihead_attention.py of fairseq.

Thank you.

Thank you. This issue really saved me lots of time .