Adding support for Rotary Position Embeddings #675
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Pulling the changes from main branch
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@dakinggg will this merge cause any problems in merging mpt code into the Hugging Face github codebase? |
vchiley
approved these changes
Oct 31, 2023
dakinggg
reviewed
Nov 1, 2023
dakinggg
approved these changes
Nov 4, 2023
Co-authored-by: Daniel King <43149077+dakinggg@users.noreply.github.com>
Co-authored-by: Daniel King <43149077+dakinggg@users.noreply.github.com>
Co-authored-by: Daniel King <43149077+dakinggg@users.noreply.github.com>
Co-authored-by: Daniel King <43149077+dakinggg@users.noreply.github.com>
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Adding the support for Rotary Positional Embeddings(RoPE).
tl;dr: Advantages of RoPE: This embedding gets applied to the query and key matrices, and hence (unlike ALiBi embeddings), is agnostic to the attention implementation and works out-of-the box for any attention implementation. Recent works have also shown that some variants of RoPE are good at extrapolating beyond training length.
Design doc
Experiments: 125M model, 1B model.