Add option to normalize loss per target #326
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thomasw21
reviewed
Aug 10, 2022
thomasw21
reviewed
Aug 16, 2022
thomasw21
reviewed
Aug 17, 2022
| @@ -95,6 +103,10 @@ def get_batch_pipe(data): | |||
| segment_ids=segment_ids.long(), | |||
| ) | |||
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| if args.norm_target_loss: | |||
| loss_mask = loss_mask.view(-1) | |||
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There's a fun hack you can do, view have the same storage space as the initial model. so you can probably write something like:
def fast_normalize(loss_mask: torch.Tensor):
"""
Turn loss_mask from [0,0,0,1,1,0,0,1,0,0,1,1,1] > [0,0,0,0.5,0.5,0,0,1,0,0,0.3,0.3,0.3]
"""
flatten_view = loss_mask.view(-1)
_, inverse_indices, counts = torch.unique_consecutive(loss_mask, return_inverse=True, return_counts=True)
counts = torch.gather(dim=0, index=inverse_indices, input=counts)
flatten_view.div_(counts)
return loss_mask
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you could also clone before doing this operation so that you actually don't make fast_normalize a in-place operation.
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Why is
def fast_normalize(loss_mask: torch.Tensor):
"""
Turn loss_mask from [0,0,0,1,1,0,0,1,0,0,1,1,1] > [0,0,0,0.5,0.5,0,0,1,0,0,0.3,0.3,0.3]
"""
flatten_view = loss_mask.view(-1)
_, inverse_indices, counts = torch.unique_consecutive(loss_mask, return_inverse=True, return_counts=True)
counts = torch.gather(dim=0, index=inverse_indices, input=counts)
flatten_view.div_(counts)
return loss_mask
better than
def fast_normalize(loss_mask: torch.Tensor):
"""
Turn loss_mask from [0,0,0,1,1,0,0,1,0,0,1,1,1] > [0,0,0,0.5,0.5,0,0,1,0,0,0.3,0.3,0.3]
"""
_, inverse_indices, counts = torch.unique_consecutive(loss_mask, return_inverse=True, return_counts=True)
counts = torch.gather(dim=0, index=inverse_indices, input=counts)
return loss_mask / counts
?
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Does the latter work if loss_mask is not 1D?
thomasw21
reviewed
Aug 17, 2022
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