LLM-Shearing
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princeton-nlp
The Project is not implemented for 70B llama?
No GQA implementation is found, so the model is not capable to scale to 70B for composerLLAMA. Maybe we need design GQA and introduce head_z for wq and head_z_kv for wk and wv?
Hi, the modeling file currently does not support GQA, but should require minimal changes to support it. What you described should work perfectly :)
It seems that we need a hierarchical pruning scheme for gqa, group pruning and head pruning inside group? Since we need to keep the number of heads in each group the same.
It seems that we need a hierarchical pruning scheme for gqa, group pruning and head pruning inside group? Since we need to keep the number of heads in each group the same.
In order to make the pruned model be able to run tp, it would be better to keep the group num unchanged.
We only need to prune the query heads for each group, thus maybe a layer_num * group_num* group_heads_query z_group_query need to initialized.
Pruning queries might cause the number of queries to be different in different groups. So maybe a group-based pruning is more reasonable? @zhangzhenyu13
Could we share the mask of query-heads among different groups?
Pruning queries might cause the number of queries to be different in different groups. So maybe a group-based pruning is more reasonable? @zhangzhenyu13
Pruning queries might cause the number of queries to be different in different groups. So maybe a group-based pruning is more reasonable? @zhangzhenyu13
Yes, your settings are right. We need to share z across groups.
Hi @zhangzhenyu13 I have some confusion. The author's composer llama file does not implement any GQA functionality. Did you implement GQA forward yourself? Which llama warehouse implementation version is better to refer to?
