[model] Reduce medusa weight

[skylee-01]

Medusa predicts N tokens in speculative decoding and trains N lheads，In actual deployments, only ResidualBlock is usually trained, not lm_head.So I just keep a copy of lm_head and share it in different heads. In practice, every lm_head saved will reduce 1G of HBM, which is crucial on graphics cards such as the 4090.At the same time, medusa can be predicted longer.

[github-actions]

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[DarkLight1337]

Is original_lm_head a common config field for this type of model? Imo if it's just for personal use, you can use OOT registration to handle this case instead of updating the main repo. (Sorry I didn't look into this deeply earlier)

[skylee-01]

Is original_lm_head a common config field for this type of model? Imo if it's just for personal use, you can use OOT registration to handle this case instead of updating the main repo. (Sorry I didn't look into this deeply earlier)

original_lm_head is only available in the medusa model. This strategy can reduce HBM very well. I think it is an optimization of usage and improves the length of medusa prediction. The original author only considered the case of qps=1, which is not consistent with the vllm scenario. In the scenario I'm exposed to, sharing lm_head is a good strategy. If necessary, I can do some experiments to test its effect.

[DarkLight1337]

For future reference, can you link an example HF repo which uses this config field?

[skylee-01]

For future reference, can you link an example HF repo which uses this config field?

I also really agree with your point.

[skylee-01]

For future reference, can you link an example HF repo which uses this config field?

Here is a config example. In medusa if your weights are not trained on lm_head, you can configure original_lm_head=true so that all heads will share the original lm_head and reduce HBM.
https://huggingface.co/SkyLee-01/Medusa/blob/main/config.json

[DarkLight1337]

Sorry for the false ping.

[DarkLight1337]

Thanks for adding this optimization!