[WIP] Attention across documents. #213
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GeorgiosSmyrnis
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| help="If set, allow model to do multiple data passes over our dataset, in order to reach the desired number of tokens.", | ||
| ) | ||
| parser.add_argument( | ||
| "--mask-across-documents", |
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i think this should be an int not a bool so that a user can specify their EOT token
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Makes sense - will update the parameter.
| if args.mask_across_documents: | ||
| # Some input samples contain EOT as the final token. The prediction after that is meaningless, so it | ||
| # should not contribute to the loss. | ||
| ignore_indices = torch.nonzero(inputs == SpecialTokens.END_OF_TEXT.value, as_tuple=True) |
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i prefer not to hard code our EOT to keep open_lm tokenizer agnostic
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Agreed - I'll change it so that it uses the user defined EOT token.
* Update .gitignore * Fix requirements for env. * Remove test data prep file erroneously committed. * Revert requirements. * Update makefile. --------- Co-authored-by: George Smyrnis <[email protected]>
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| # Some input samples contain EOT as the final token. The prediction after that is meaningless, so it | ||
| # should not contribute to the loss. | ||
| ignore_indices = torch.nonzero(inputs == SpecialTokens.END_OF_TEXT.value, as_tuple=True) | ||
| targets = targets.detach().clone() # Clone this because it shares mem with input! |
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Interesting, is the detach necessary here? When args.mask_across_documents is False, should we also a detach()?
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Detach is not necessary, but clone is - because the targets and the input share the underlying tensor, if the target is explicitly set then the input is also affected.
When args.mask_across_documents is False, this is not an issue - neither the target nor the input are explicitly changed.
This adds a flag that stops attention from going across documents, identified by the EOT token.
The loss for the token right after the EOT token is ignored.
TODO: add some tests for the shape of the mask.