[WIP] support Medusa

[zhyncs]

Motivation

As titled, support Medusa

Modification

finished

• 1、Medusa weights conversion
• 2、Medusa weights loading
• 3、Porting Medusa Heads code with LMDeploy components and utilities
• 4、TP support: Distribute the weights equally based on hidden_size

We've used https://github.com/zhyncs/medusa-whl-centos7/releases/tag/2024.02.27, https://huggingface.co/FasterDecoding/medusa-vicuna-13b-v1.3, https://huggingface.co/lmsys/vicuna-13b-v1.3 to verify the correctness of porting code (fp16 and bf16).

during debugging

• 1、Porting generate_candidates and evaluate_posterior
• 2、Integrating with LlamaBatch

todo

• 1、add docs
• 2、add tests
• 3、benchmark

Checklist

1. Pre-commit or other linting tools are used to fix the potential lint issues.
2. The modification is covered by complete unit tests. If not, please add more unit tests to ensure the correctness.
3. If the modification has a dependency on downstream projects of a newer version, this PR should be tested with all supported versions of downstream projects.
4. The documentation has been modified accordingly, like docstring or example tutorials.

[zhyncs]

Hi @lvhan028 May you help change the base branch to turbomind-2.1 for this pr? Thanks.

[zhyncs]

Hi all. We'v tested the acc rate of LM Head temperature 0 and Medusa Heads top 1 in the internal version, whether it is custom prompts or MT-Bench, the acc rate is relatively low, only 0-20% and the vast majority is 0. In this case, we did not achieve the desired benefits. At the same time, we verified LM Head temperature 0 and Medusa Heads top k in the official version with medusa choices(64). The acc rate is between 20%-40%, which is closer to the greedy data in the paper. Considering that verifying Medusa Choices using a multi-batch approach would incur significant costs, we have decided to implement a Tree Mask version based on Flash Decoding on this basis. We will provide technical solutions aligning with @lzhangzz as soon as possible. Please stay tuned for updates.