Slight modifications to alibi which allow for bidirectional position embedding #88
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@jstjohn ohh yea, this makes sense, but i do account for it https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py#L797 already the main reason i kept the base Alibi the way it is, is because Ofir's original code was like that |
Contributor
Author
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There is another issue though that I didn’t call out. The bidirectional=True version has the opposite pattern you would want. The upper and lower corner has the max rather than the max being along the diagonal. So this also fixes that issue.
…On May 15, 2022, 1:26 PM -0700, Phil Wang ***@***.***>, wrote:
@jstjohn ohh yea, this makes sense, but i do account for it https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py#L797 already
the main reason i kept the base Alibi the way it is, is because Ofir's original code was like that
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Contributor
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But then again since bidirectional and non bidirectional should be equivalent when implemented and masked, it seems easiest to drop that option and just implement it in the general way? |
Owner
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@jstjohn yes that is true, lets go for your way, thank you for the PR! |
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Changes to AlibiPositionalBias
Alibi weights before change were only appropriate if an upper triangular mask is applied on the qk dot product:
Alibi weights after change are now appropriate for bidirectional attention without a mask, and should be equivalent with a mask:
Changes to LearnedAlibiPositionalBias
Alibi weights before change were not maximal on the diagonal if the full attention matrix in unmasked form was presented, so they were only usable with an upper triangular mask:
After the change Alibi weights in the learned module match the base module