[AMD] Add MiniMax-M3-FP4 MI355X ATOMMESH#1856
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Thanks for the contribution! For vLLM & SGLang, please ensure that your recipes is similar to the official vLLM recipes and/or the SGLang cookbook 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 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 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 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。 |
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Thanks for the contribution! For vLLM & SGLang, please ensure that your recipes is similar to the official vLLM recipes and/or the SGLang cookbook 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 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 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 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。 |
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m3 vllm disagg seems a bit more ready and doesnt break the other vllm disagg models. so prob ship that first before this one?
also left some comments, plz test that it doesnt break dsv4
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27834600324 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27853077483 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27853077483 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27853680451 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27854245106 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27857373443 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27857373443 |
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… disagg - server_atom.sh: temp patch custom_all_reduce.py from Jasen/fix_custom_allreduce branch on all nodes before server launch - minimaxm3_fp4_mi355x_atom-disagg.sh: unconditionally set MAX_MODEL_LEN=32768 so CI-computed isl+osl+256 does not override the recipe default Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Replace curl with git init+fetch to pull a specific commit (6af67c273d774198e9f5815dbaa93991ffc69602) from ROCm/aiter main, using sparse-checkout to fetch only the target file. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27956325179 |
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- amd-master.yaml: bump minimaxm3-fp4-mi355x-atom-disagg image to rocm/atom-dev:vllm-v0.22.0-nightly_20260622 - server_atom.sh: remove temp custom_all_reduce.py patch (fixed in new image), minor comment cleanup Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27992408762 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27992760383 |
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=27995429049 |
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Summary
minimaxm3_fp4_mi355x_atom-disagg.sh: new CI entry point for MiniMax-M3-MXFP4 multi-node disaggregated PD on MI355X via ATOM, mirroring the dsv4 disagg script. No MTP, KV_CACHE_DTYPE=auto (MXFP4 format, no fp8 override), MAX_MODEL_LEN/MAX_NUM_BATCHED_TOKENS=32768server_atom.sh: make--kv_cache_dtypeconditional (skipped when empty orauto); addMAX_MODEL_LEN,MAX_NUM_BATCHED_TOKENS,CUDAGRAPH_OPTsupport; fix"${ARRAY[@]}"broken-quote array splice inside eval'd CMD strings; extract--hf-overridesintoHF_OVERRIDES_ARG(DeepSeek-V4-Pro only); addOpt argsline to INFO blockjob.slurm: passMAX_MODEL_LEN,MAX_NUM_BATCHED_TOKENS,CUDAGRAPH_OPTthrough Docker env foratom-disaggenginebench.sh: use--dsv4flag for DeepSeek-V4-Pro MTP runs (instead of--use-chat-template)SPEC_DECODING,DECODE_MTP_SIZE,BENCH_REQUEST_RATE,IS_MTPTest plan
minimaxm3-fp4-mi355x-atom-disaggsweep launches correctly on MI355X--kv_cache_dtypeis absent from MiniMax-M3 server launch logs--kv_cache_dtype fp8is present for DSv4 server launch logsMAX_MODEL_LEN/MAX_NUM_BATCHED_TOKENSappear in server args when set🤖 Generated with Claude Code
Note
Medium Risk
Touches shared
server_atom.shand Slurm/docker env for allatom-disaggjobs (including DSv4), so regressions in server flags or eval command construction could break existing disagg sweeps—not production serving, but high-impact for CI benchmarks.Overview
Adds MiniMax-M3-MXFP4 multi-node prefill/decode on MI355X via ATOM/atommesh (
minimaxm3-fp4-mi355x-atom-disagginamd-master.yamlplusminimaxm3_fp4_mi355x_atom-disagg.sh), with 1P1D TP4 sweeps at 1k/1k and 8k/1k, no MTP, native MXFP4 KV (KV_CACHE_DTYPE=auto), block size 128, andMAX_MODEL_LEN/MAX_NUM_BATCHED_TOKENSat 32768.Shared ATOM disagg plumbing is extended so MiniMax can run without breaking DeepSeek-V4-Pro:
server_atom.shrenamesMEM_FRACTION→MEM_FRAC_STATIC, omits--kv_cache_dtypewhen dtype is empty/auto, threads optional length/batch caps and MTP flags into prefill/decodeevalcommands (fixing broken"${ARRAY[@]}"inside eval strings), gates DSv4-only--hf-overrides, TBO, and env tweaks vsAITER_QUICK_REDUCE_QUANTIZATION=INT4for other models, and setsIS_MTPfor benchmarking.job.slurmpassesBENCH_REQUEST_RATEand the new atom env vars;env_atom.shapplies MoE interleave/bound overrides only forDeepSeek-V4-Pro;bench.shuses--dsv4for DSv4 MTP runs;models_atom.yamlregisters MiniMax-M3 MXFP4/MXFP8 stubs.Reviewed by Cursor Bugbot for commit 3a7ab53. Bugbot is set up for automated code reviews on this repo. Configure here.