Skip to content

[AMD][MI355X] Bump MiniMax-M3 AgentX to latest ROCm nightly - #2959

Merged
cquil11 merged 15 commits into
mainfrom
amd/mm3-mi355x-agentx-nightly-20260910
Sep 14, 2026
Merged

cquil11 merged 15 commits into
mainfrom
amd/mm3-mi355x-agentx-nightly-20260910

Conversation

@ukannika

@ukannika ukannika commented Sep 10, 2026

Copy link
Copy Markdown
Collaborator

Note

Medium Risk
Changes benchmark serving flags (KV layout, FP8 indexer, higher GPU memory use, CUDA graph capture) and narrows the official concurrency matrix, which can shift measured AgentX curves and stability on TP2.

Overview
Bumps the MI355X MiniMax-M3 AgentX vLLM+MTP recipe to a newer immutable ROCm nightly and retunes serving for the optimized FP8 AITER indexer path.

The launch script always enables VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT (drops the old TP2 vs TP4 branch), raises --gpu-memory-utilization to 0.90, adds a hand-tuned cudagraph_capture_sizes list under FULL_DECODE_ONLY, and passes --attention-config with indexer_kv_dtype: fp8. The amd-master config pins the new image digest and trims agentic concurrency sweeps (fewer points on TP4; TP2 capped at conc 1–2). perf-changelog.yaml records the image bump and optimization notes.

Reviewed by Cursor Bugbot for commit 4bcd19a. Bugbot is set up for automated code reviews on this repo. Configure here.

@cursor cursor Bot left a comment

Copy link
Copy Markdown

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Cursor Bugbot has reviewed your changes using default effort and found 1 potential issue.

Fix All in Cursor

❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.

Want higher recall? High effort reviews run extra passes and find more bugs. A team admin can switch effort levels in the Cursor dashboard.

Reviewed by Cursor Bugbot for commit 8da0f81. Configure here.

Comment thread benchmarks/single_node/agentic/minimaxm3_fp4_mi355x_mtp.sh
@github-actions

Copy link
Copy Markdown
Contributor

Comment thread benchmarks/single_node/agentic/minimaxm3_fp4_mi355x_mtp.sh
Comment thread perf-changelog.yaml Outdated
@github-actions

Copy link
Copy Markdown
Contributor

@github-actions

Copy link
Copy Markdown
Contributor

@github-actions

Copy link
Copy Markdown
Contributor

@github-actions

Copy link
Copy Markdown
Contributor

1 similar comment
@github-actions

Copy link
Copy Markdown
Contributor

@github-actions

Copy link
Copy Markdown
Contributor

@github-actions

Copy link
Copy Markdown
Contributor

1 similar comment
@github-actions

Copy link
Copy Markdown
Contributor

@github-actions

Copy link
Copy Markdown
Contributor

@chunfangamd

Copy link
Copy Markdown
Collaborator

/stage-results 34609443618

@github-actions

github-actions Bot commented Sep 12, 2026

Copy link
Copy Markdown
Contributor

@chunfangamd staged run 34609443618: https://inferencemax-app-git-staging-semianalysisai.vercel.app/inference?i_dates=2026-09-11~r34609443618

This run remains available across future /stage-results requests. Staging the same run ID again updates its staged data. Staging workflow

@chunfangamd

Copy link
Copy Markdown
Collaborator

/reuse-sweep-run 34609443618

@seungrokj seungrokj left a comment

Copy link
Copy Markdown
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

As a PR reviewer and CODEOWNER, I have reviewed this and have:

  • 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
  • For agentic workloads: verified that speculative-decoding configs (EAGLE / MTP / draft models) run with simulated synthetic acceptance, with the acceptance-length value taken from the committed golden AL curve in golden_al_distribution/ for that model, thinking mode, and draft length. A submission may choose any supported draft length, but it may not substitute a different acceptance target.
  • Verified against the current MODELS.md that this PR does not submit a deprecated model, scenario, or model-scenario combination.
  • Verified that the model architecture isn't changed with benchmark hacks like using --hf-overrides to skipping indexer for every x layers on models that don't natively support this. As a general rule, we won't accept optimizations that reduces the number of model architecture FLOPs. Anything that makes that same computation run faster is fair game; FLOPs at lower precisions is fine, given that the config passes private evals. As an general north star princple, we should only use optimizations which is used in production by customers that care about accuracy
  • If an company claims that they support vLLM/SGLang as first class LLM inference engines on their hardware, I have verified that the respective vLLM submission made using upstream https://hub.docker.com/u/vllm docker repo, upstream SGLang https://hub.docker.com/u/lmsysorg docker repo. 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 as supported by vLLM/SGLang community maintainers
  • If an company claims that they support vLLM/SGLang as first class upstream in-tree LLM inference engines on their hardware, I have 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 every single-node vLLM/SGLang recipe in this PR is documented in the official vLLM recipes and/or the SGLang cookbook:
    • I linked the corresponding upstream PR in the vLLM recipe repo or SGLang repo and verified that it is MERGED before this InferenceX PR merges. An opened, draft, or closed-without-merge upstream PR does not satisfy this requirement. If the matching recipe was already published, I linked the published recipe/cookbook page in the additional detail section below.
  • Verified that this PR does not patch the inference engine or serving stack — the pinned image must run as shipped. This covers .patch files / git apply / patch, inline patches embedded in benchmark scripts (e.g. a python3/sed heredoc that rewrites installed engine sources before serving), in-place edits of site-packages, monkey-patching, overwriting container files, and installing forked/rebuilt engine wheels on top of the pinned image. The only exception is a patch covered by a filled-out waiver at docs/waiver/<PR_NUMBER>.md — named after the PR that introduces the patch and filed in that same PR, stating what is patched, why the unmodified upstream image cannot run this benchmark, the upstream PR/issue link, and the removal plan — which I have linked below in the additional detail section.
  • If this PR uses append-only: true, verified that it only adds generated points or recipe variants inside a selected existing config/scenario and existing same-image visual curve: every previously generated point remains present with the same recipe, no prior point is removed or rerun, and every benchmark-affecting change in the complete diff can affect only the corresponding newly appended points (never an existing point), regardless of which file contains it.
  • If any of the above criteria cannot reasonably be satisfied, I have provided additional reasoning below.

Additional detail section:

Signed: seungrokj

@Klaud-Cold

Copy link
Copy Markdown
Collaborator

✅✅✅ Verdict: PASS ✅✅✅

✅ Check 0 (CODEOWNER): PASS — @seungrokj is a listed owner of configs/amd-master.yaml; the other two changed paths fall under the catch-all only.
✅ Check 1 (Passing sweep on in-PR commit): PASS — run 34609443618 on in-PR commit c738b503 has all 12 agentic / and all 12 agentic eval / check-runs at success (fixed-seq single-node */ and eval / skipped because the PR touches only the agentic config).
✅ Check 2 (Evals pass): PASS — agg_eval_all.json from that run has 12/12 gsm8k points (TP4 c1–c32, TP2 c1–c2) with em_strict 0.964–0.970 against the 0.90 thresholds.yaml floor, all on image vllm/vllm-openai-rocm:nightly-2a02f6ef…, the same image this PR pins.
✅ Check 3 (Recipe linked, merged, complete): PASS — recipes.vllm.ai MiniMax-M3 mi355x/mxfp4/spec_decoding is published; its source models/MiniMaxAI/MiniMax-M3.yaml on vllm-project/recipes main (#970, merged 2026-09-14) carries an InferenceX MI355X reproduction section whose serve command matches this PR on every major arg: amd/MiniMax-M3-MXFP4, TP4/TP2, --kv-cache-dtype fp8, --attention-backend ROCM_AITER_UNIFIED_ATTN, --moe-backend aiter, --attention-config indexer_kv_dtype fp8, EAGLE3 Inferact/MiniMax-M3-EAGLE3-GQA with 3 tokens, and the AITER env vars. Capture-size list and --gpu-memory-utilization also match (informational).
✅ Check 4 (Reuse command): PASS — /reuse-sweep-run 34609443618 posted by chunfangamd (COLLABORATOR).
✅ Check 5 (Latest checklist template): PASS — all 15 current template items present and checked.
✅ Check 6 (Upstream image, engine-first): PASS — framework: vllm with vllm/vllm-openai-rocm:nightly-2a02f6ef… from the upstream vllm/ org; ordering check not applicable to a vLLM entry.
✅ Check 7 (No deprecated models/scenarios): PASS — minimaxm3 agentic-coding EAGLE3 arm is active in MODELS.md as of 2026-09-14.
✅ Check 8 (No architecture hacks): PASS — no --hf-overrides or config edits; the added indexer_kv_dtype: fp8 is a precision change, not a FLOP reduction, and evals pass.
✅ Check 9 (Spec-decode via chat template): PASS — replay adds --apply-chat-template and the server pins --default-chat-template-kwargs.
✅ Check 10 (No engine patches): PASS — no patch files, heredoc rewrites, or engine wheel installs in the diff or script.
✅ Check 11 (Agentic spec-decode golden AL): PASS — throughput arm uses rejection_sample_method: synthetic with synthetic_acceptance_length: 2.78, equal to minimaxm3_eagle3_gqa.yaml thinking_on[3]; eval arm uses real verification.
➖ Check 12 (Append-only): N/A — the new perf-changelog.yaml entry does not set append-only: true.

Note (non-blocking): the changelog description says max-num-batched-tokens was raised to 65536, but the script keeps 32768.

@seungrokj

Copy link
Copy Markdown
Collaborator

@Oseltamivir @adibarra can you plz review this ?

@Oseltamivir Oseltamivir left a comment

Copy link
Copy Markdown
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

lgtm

@cquil11
cquil11 merged commit 8cd9b38 into main Sep 14, 2026
31 checks passed
@cquil11
cquil11 deleted the amd/mm3-mi355x-agentx-nightly-20260910 branch September 14, 2026 13:24
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Projects

Status: Done

Development

Successfully merging this pull request may close these issues.

6 participants