Comments (7)
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.
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Hi, the modeling file currently does not support GQA, but should require minimal changes to support it. What you described should work perfectly :)
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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.
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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.
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Pruning queries might cause the number of queries to be different in different groups. So maybe a group-based pruning is more reasonable? @zhangzhenyu13
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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
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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?
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Related Issues (20)
- missmatch shape
- Start training but nothing continue HOT 6
- TypeError: buffer is too small for requested array
- Pruning fine-tuned model HOT 2
- save model meet problem HOT 1
- Instruction tuning dataset HOT 2
- If I can't configure Slurm on a cluster, does that mean I can't use multi-node multi-GPU setups? HOT 5
- 有没有不用Slurm跑剪枝的方法?
- None
- Start training but only output config information HOT 3
- LlamaRMSNorm() layer differs from original llama HOT 1
- composer model trans to pythia problem
- The dtype of tokenized data should be uint32 HOT 1
- Why the rope params are ignored while converting hf checkpoint to composer checkpoint? HOT 3
- about shearing params config HOT 1
- Can LLM-Shearing be used on ViT models? HOT 1
- Support for Llama-3 / GQA? HOT 1
- Open source the pruning mask. HOT 2
- Default Initialization of Lambda Parameters to Zero HOT 3
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