[AMD] MiniMax-M3 FP4/FP8 MI355X ATOMESH (disagg): refactor config & add MTP recipes / 重构配置并新增 MTP 配方 / 설정 리팩토링 및 MTP 레시피 추가

[seungrokj]

Summary

• Refactor server_atom.sh and models_atom.yaml to centralize model-specific ATOM config (block size, memory fraction, model length, quant config, hf overrides) and support per-role (prefill/decode) flags for TP+DPA, EP+DPA, and online_quant_config
• Move hardcoded server tuning params from launch scripts into models_atom.yaml with env-var override support
• Add minimaxm3-fp4/fp8-mi355x-atom-disagg-mtp recipes to amd-master.yaml for EAGLE3 speculative decoding (DECODE_MTP_SIZE=3)
• Remove redundant SPEC_DECODING gating; use MODEL_MTP_FLAGS + DECODE_MTP_SIZE > 0 directly
• Split online_quant_config into DPA and non-DPA variants (FP8 uses different exclude patterns with DPA)

PR Review Checklist

• Verified that as of the moment of typing this, this is the latest version of PR_REVIEW_CHECKLIST.md
• Verified that the general code quality meets the InferenceX standard and does not make the code quality any worse.
• Verified that this PR has passed PR validation. Please link to GitHub Action workflow that shows this.
• Verified that this PR passes evals. Please link to GitHub Action workflow that shows this.
• Verified that speculative decoding PRs uses chat templates to align the AL distribution to real world
• If a company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM/SGLang submission has been made before additional frameworks (TRT-LLM, ATOM, etc.). The only exceptions are for new hardware, such as MI455X UALoE72, Vera Rubin NVL72, Rubin NVL8, etc., and for new model architectures where there is an actual reason why vLLM/SGLang does not fundamentally support them yet.
• Verified that the single-node recipes are similar to the official vLLM recipes and/or the SGLang cookbook:
  • If they are not, I have verified that a PR has been opened in vLLM recipe repo or SGLang repo and linked it below in the additional detail section:
• If any of the above criteria cannot reasonably be satisfied, I have provided additional reasoning below.

🤖 Generated with Claude Code

中文说明

重构 server_atom.sh 和 models_atom.yaml，将模型特定的 ATOM 配置（block size、内存比例、模型长度、量化配置、hf overrides）集中管理，支持按角色（prefill/decode）分别设置 TP+DPA、EP+DPA 和 online_quant_config 参数。将启动脚本中的硬编码调优参数迁移到 models_atom.yaml，支持环境变量覆盖。新增 minimaxm3-fp4/fp8-mi355x-atom-disagg-mtp 配置到 amd-master.yaml，支持 EAGLE3 投机解码（DECODE_MTP_SIZE=3）。移除冗余的 SPEC_DECODING 判断，改用 MODEL_MTP_FLAGS + DECODE_MTP_SIZE > 0 直接控制。将 online_quant_config 拆分为 DPA 和非 DPA 两个变体（FP8 在 DPA 模式下使用不同的 exclude pattern）。

한국어 설명

server_atom.sh와 models_atom.yaml을 리팩토링하여 모델별 ATOM 설정(block size, 메모리 비율, 모델 길이, 양자화 설정, hf overrides)을 중앙 집중화하고, 역할별(prefill/decode) TP+DPA, EP+DPA, online_quant_config 플래그를 지원합니다. 런치 스크립트의 하드코딩된 서버 튜닝 파라미터를 models_atom.yaml로 이전하고 환경 변수 오버라이드를 지원합니다. amd-master.yaml에 minimaxm3-fp4/fp8-mi355x-atom-disagg-mtp 레시피를 추가하여 EAGLE3 투기적 디코딩(DECODE_MTP_SIZE=3)을 지원합니다. 불필요한 SPEC_DECODING 게이팅을 제거하고 MODEL_MTP_FLAGS + DECODE_MTP_SIZE > 0으로 직접 제어합니다. online_quant_config를 DPA/비-DPA 변형으로 분리합니다 (FP8은 DPA 모드에서 다른 exclude 패턴 사용).

[github-actions]

Thanks for the contribution! For vLLM & SGLang, please ensure that your recipes is similar to the official vLLM recipes and/or the SGLang cookbook

• https://docs.sglang.io/cookbook/intro
• https://recipes.vllm.ai/

If it is not, please create a PR first before we can merge your single node PR into the master branch. Let's ensure that the documentation is first class such that the entire ML community can benefit from your hard work! Thank you

• https://github.com/vllm-project/recipes
• https://github.com/sgl-project/sglang/tree/main/docs_new

PR authors are responsible for ensuring that after merging, all GitHub Action jobs fully pass. A lot of the time, failures are just flakes and simply re-running the failed jobs will fix it. If re-running failed jobs is attempted, PR authors are responsible for ensuring it passes. See GitHub's docs on re-running failed jobs: https://docs.github.com/en/actions/how-tos/manage-workflow-runs/re-run-workflows-and-jobs#re-running-failed-jobs-in-a-workflow

As a rule of thumb, generally, PR authors should request a review & get a PR approval from the respective companies' CODEOWNERS before requesting a review from core maintainers.

If additional help is needed, PR authors can reach out to core maintainers over Slack.

---

感谢你的贡献！对于 vLLM 与 SGLang，请确保你的 recipe 与官方 vLLM recipes 和/或 SGLang cookbook 保持一致

• https://docs.sglang.io/cookbook/intro
• https://recipes.vllm.ai/

如果不一致，请先创建一个 PR，之后我们才能将你的单节点 PR 合并到 master 分支。让我们确保文档保持一流水准，使整个 ML 社区都能从你的辛勤工作中受益！谢谢

• https://github.com/vllm-project/recipes
• https://github.com/sgl-project/sglang/tree/main/docs_new

PR 作者有责任确保合并后所有 GitHub Action 任务完全通过。 很多时候失败只是偶发抖动（flake），重新运行失败的任务即可解决。如果选择重新运行失败的任务，PR 作者有责任确保其最终通过。参见 GitHub 关于重新运行失败任务的文档：https://docs.github.com/en/actions/how-tos/manage-workflow-runs/re-run-workflows-and-jobs#re-running-failed-jobs-in-a-workflow

一般而言，PR 作者应先向相应公司的 CODEOWNERS 请求审阅并获得 PR 批准，然后再请求核心维护者审阅。

如需更多帮助，PR 作者可通过 Slack 联系核心维护者。

[github-actions]

see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=28645608307
see unofficial run visualizer at https://inferencex.semianalysis.com/evaluation?unofficialRun=28645608307

[github-actions]

see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=28645608307
see unofficial run visualizer at https://inferencex.semianalysis.com/evaluation?unofficialRun=28645608307