Thanks for the contribution! Please reach out to respective companies' CODEOWNER to fill in the latest PR_REVIEW_CHECKLIST.md before pinging core maintainer on Slack for review. In order for the signoff PR check bot to trigger, you must follow the PR_REVIEW_CHECKLIST.md template correctly, including the phrase As a PR reviewer and CODEOWNER, I have reviewed this and have.

For PR verification, add the full-sweep-fail-fast label (strongly recommended) to this PR — the benchmark sweep only runs on labeled PRs. Use full-sweep-enabled only if you need matrix jobs to keep running past a failure.

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. See GitHub's docs on re-running failed jobs

感谢你的贡献！请联系相应公司的 CODEOWNER 填写最新的 PR_REVIEW_CHECKLIST.md，然后再在 Slack 上联系核心维护者进行审阅。为了触发 signoff PR 检查机器人，你必须正确遵循 PR_REVIEW_CHECKLIST.md 模板，包括保留英文语句 As a PR reviewer and CODEOWNER, I have reviewed this and have。

如需进行 PR 验证，请为此 PR 添加 full-sweep-fail-fast 标签（强烈推荐）— 基准测试 sweep 仅在带有标签的 PR 上运行。仅当需要矩阵任务在失败后继续运行时才使用 full-sweep-enabled。

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。参见 GitHub 关于重新运行失败任务的文档

see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=29854639662
see unofficial run visualizer at https://inferencex.semianalysis.com/evaluation?unofficialRun=29854639662

see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=29855213440
see unofficial run visualizer at https://inferencex.semianalysis.com/evaluation?unofficialRun=29855213440

vllm-project/recipes#660

see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=29896096279
see unofficial run visualizer at https://inferencex.semianalysis.com/evaluation?unofficialRun=29896096279

see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=29953041481
see unofficial run visualizer at https://inferencex.semianalysis.com/evaluation?unofficialRun=29953041481

/reuse-sweep-run

❌❌❌ REJECTED ❌❌❌

@chunfangamd — blocking: the linked recipe documents a different model. This PR benchmarks amd/Kimi-K2.5-MXFP4, but vllm-project/recipes#660 documents amd/MiniMax-M3-MXFP4. No merged/published upstream recipe reproduces the Kimi-K2.5 MI355X recipe, so Check 3 fails.

➖ Check 0 (CODEOWNER): PASS — @chunfangamd owns configs/amd-master.yaml; the script + perf-changelog.yaml are catch-all (* @InferenceX/core) covered by any CODEOWNER.
✅ Check 1 (green in-PR sweep+eval): PASS — head 45c6584 has executed, green single-node 8k1k / and eval / jobs in run 29953041481 (1k1k skip is expected — the config sweeps only 8k1k).
✅ Check 2 (eval accuracy): PASS — GSM8K em_strict 96.89%–97.42% (n=1319) on amd/Kimi-K2.5-MXFP4, same image vllm/vllm-openai-rocm:nightly-387189c... as this PR's config.
❌ Check 3 (recipe merged+complete): FAIL — recipe #660 is MERGED but documents amd/MiniMax-M3-MXFP4, not this PR's amd/Kimi-K2.5-MXFP4; model/model-path is a MAJOR arg and does not match (recipe is MiniMax-specific: --tool-call-parser minimax_m3, --reasoning-parser minimax_m3). Link the merged Kimi-K2.5 recipe. (The PR's own draft blocker "Finish/update the matching upstream recipes documentation" is unchecked.)
✅ Check 4 (reuse command): PASS — /reuse-sweep-run posted by @chunfangamd (COLLABORATOR).
✅ Check 5 (latest template): PASS — all current-template items present; unchecked items (agentic spec-decode, catch-all reasoning) are non-applicable conditionals.
✅ Check 6 (upstream image / engine-first): PASS — vllm/vllm-openai-rocm:nightly-387189c... is upstream vLLM org; framework vllm on established SKU MI355X. Engine-first N/A (vLLM entry).
✅ Check 7 (no arch hacks): PASS — no --hf-overrides; fp8 KV-cache, block-size, batched-tokens are serving config, not FLOPs reduction.
➖ Check 8 (spec-decode chat template): N/A — no speculative-decoding changes.
✅ Check 9 (no engine patch): PASS — env-var exports and serve args only; no patch/heredoc/wheel rewrite of the engine.
➖ Check 10 (agentic golden AL): N/A — no agentic speculative-decoding changes.

vllm-project/recipes#673

@claude check the recipe RP: vllm-project/recipes#673

Claude finished @chunfangamd's task in 1m 19s —— View job

Recipe PR check — vllm-project/recipes#673

• Read vllm-project/recipes#673
• Compare against this PR's amd/Kimi-K2.5-MXFP4 requirement (Check 3)
• Report verdict

✅ This resolves the Check 3 blocker

Recipe #673 — "[ROCm] Document Kimi-K2.5 MXFP4 MI355X benchmark reproduction" — is MERGED (merged 2026-07-23T08:19:11Z by @xianbaoqian, merge commit 0885682), and unlike the previously-linked #660 it documents the correct model.

This is the fix for the earlier Klaud-Cold rejection, which failed Check 3 solely because #660 documented amd/MiniMax-M3-MXFP4. #673 adds a dedicated "MXFP4 benchmark reproduction (InferenceX MI355X sweep)" section to the Kimi-K2.5 recipe.

Settings cross-check (recipe vs. this PR's config)

The recipe's reproduction section matches this PR's effective settings:

• Env vars all present and equal: VLLM_ROCM_USE_AITER=1, VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=INT4, VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1, VLLM_ROCM_USE_SKINNY_GEMM=0, VLLM_ROCM_USE_AITER_RMSNORM=0, AITER_MXFP4_INTERMEDIATE=1, AITER_BYPASS_TUNE_CONFIG=0, AITER_MOE_SORT_BACKEND=auto, OMP_NUM_THREADS=1. ✅
• Serve args match: --tensor-parallel-size 4, --kv-cache-dtype fp8, --block-size 16, --max-num-batched-tokens 16384, --max-num-seqs 512, --async-scheduling, --no-enable-prefix-caching, --mm-encoder-tp-mode data. ✅
• MAX_MODEL_LEN >= 9472 guard documented, matching the 1k/1k validated server. ✅
• Uses upstream vllm/vllm-openai-rocm:nightly. ✅

One minor discrepancy (non-blocking)

The merged recipe reproduction command shows --gpu-memory-utilization 0.85, whereas this PR's body and config state 0.90. This is not a Check-3 concern (model/model-path is the MAJOR arg and matches), but for exact reproduction fidelity you may want to align the recipe's gpu-memory-utilization with the benchmarked 0.90, or note why the sweep uses 0.90 while the recipe documents 0.85.

Bottom line: #673 satisfies the merged-upstream-recipe requirement for amd/Kimi-K2.5-MXFP4 that #660 did not. The draft blocker "Finish/update the matching upstream recipes documentation" can now be checked, and a re-run of the Klaud-Cold gate should clear Check 3. Worth updating the linked recipe in the PR checklist from #660 → #673.
· branch perf/kimi-k2.5-mi355x-tp4

@claude I approve this PR because the reported issue has been resolved.

Signed: @chunfangamd

Claude finished @chunfangamd's task in 1m 42s —— View job

Approval acknowledged — one step still needed to flip the red check

Thanks @chunfangamd. I've verified the resolution, and the underlying blocker is resolved — but the codeowner-signoff-verify status check is still red, and a comment approval alone won't turn it green. Here's the exact state.

• Fetch base branch and inspect current PR diff
• Verify recipe [AMD] Add ATOM+MTP, dsr1 fp8/fp4 #673 resolves the Check 3 blocker (correct model)
• Check whether the PR checklist still references the stale recipe Enable fast mode for Claude Code workflows #660
• Report status back to @chunfangamd

What's resolved ✅

• Recipe (Check 3): vllm-project/recipes#673 is MERGED (merge commit 0885682, by @xianbaoqian) and documents the correct model — models/moonshotai/Kimi-K2.5.yaml for amd/Kimi-K2.5-MXFP4. This is the fix for the earlier Klaud-Cold rejection, which failed only because Enable fast mode for Claude Code workflows #660 documented amd/MiniMax-M3-MXFP4.
• Official image: configs/amd-master.yaml now pins the upstream vllm/vllm-openai-rocm:nightly-387189c… build (no more image: TBD), satisfying the engine-first / upstream-image requirement.
• Settings ↔ recipe match: the script's effective settings (--kv-cache-dtype fp8, --block-size 16, --max-num-batched-tokens 16384, --max-num-seqs 512, --async-scheduling, --gpu-memory-utilization 0.85, the AITER env vars, and the MAX_MODEL_LEN >= 9472 guard) line up with the merged recipe. Note the earlier "0.90 vs 0.85 discrepancy" is now gone — commit 19100b1 lowered util to 0.85, matching [AMD] Add ATOM+MTP, dsr1 fp8/fp4 #673.

What still blocks the green check ⚠️

The commit status codeowner-signoff-verify on head 45c6584 is failure, pointing at the Klaud-Cold rejection from 2026-07-23 05:40 — that verdict was computed before #673 merged (08:19), and against your APPROVED checklist whose Additional detail section still links the wrong recipe:

### Additional detail section:
https://github.com/vllm-project/recipes/pull/660   ← MiniMax-M3, stale

To clear the gate:

1. Update the sign-off checklist so the Additional detail section links [AMD] Add ATOM+MTP, dsr1 fp8/fp4 #673 instead of Enable fast mode for Claude Code workflows #660, and check the draft blocker "Finish/update the matching upstream recipes documentation." Fix this →
2. Re-run the sign-off verification (e.g. re-post /reuse-sweep-run or re-trigger the Klaud-Cold gate) so it re-evaluates against the now-merged [AMD] Add ATOM+MTP, dsr1 fp8/fp4 #673. Check 3 should then pass, and with Checks 0–2 and 4–9 already green the status should flip to success.

One note on the PR body

The PR description still reads image: TBD and cites --block-size 16 … 0.90 / 1k/1k local validation, but the branch has since pinned the official nightly, lowered util to 0.85, and (per perf-changelog.yaml) deprecated 1k1k in favor of the 8k1k sweep (#2263). Worth refreshing the body so it matches the merged config — cosmetic, not a merge blocker.

I can't approve the PR myself (that's disabled for the bot for security reasons), but once the checklist link is corrected and the sign-off gate re-runs, this should be green and mergeable.
· branch perf/kimi-k2.5-mi355x-tp4

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. Link: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/29953041481
• Verified that this PR passes evals. Please link to GitHub Action workflow that shows this. Range: 96.89%-97.42%, link: https://github.com/SemiAnalysisAI/InferenceX/actions/runs/29953041481
• 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 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 any of the above criteria cannot reasonably be satisfied, I have provided additional reasoning below.

Additional detail section:

• insert any additional info here
  [ROCm] Document Kimi-K2.5 MXFP4 MI355X benchmark reproduction vllm-project/recipes#673
  Signed: @chunfangamd

✅✅✅ Verdict: PASS ✅✅✅

Supersedes the earlier REJECTED verdict on this SHA: that gate ran before vllm-project/recipes#673 merged and against the stale #660 link. The current sign-off links #673 (now MERGED), which documents the correct model, so Check 3 now passes. Green run: 29953041481.

✅ Check 0 (CODEOWNER): PASS — @chunfangamd owns configs/amd-master.yaml; the script + perf-changelog.yaml are catch-all (* @InferenceX/core), covered by any CODEOWNER.
✅ Check 1 (green in-PR sweep+eval): PASS — head 45c6584 has executed, green single-node 8k1k / (TP4/TP8) and per-config eval / jobs in run 29953041481 (1k1k skip is expected — config sweeps 8k1k only).
✅ Check 2 (eval accuracy): PASS — GSM8K em_strict 96.89%–97.42% (n=1319) on amd/Kimi-K2.5-MXFP4, same image vllm/vllm-openai-rocm:nightly-387189c… as this PR's config.
✅ Check 3 (recipe merged+complete): PASS — recipes#673 is MERGED (2026-07-23) and documents amd/Kimi-K2.5-MXFP4 (MI355X TP4 sweep). Major args match: model, TP4, kv-cache-dtype fp8, block-size 16, batched-tokens 16384, max-num-seqs 512, async-scheduling, gpu-mem-util 0.85, and the AITER MXFP4 env vars.
✅ Check 4 (reuse command): PASS — /reuse-sweep-run posted by @chunfangamd (COLLABORATOR).
✅ Check 5 (latest template): PASS — all current-template items present; the two unchecked items (agentic spec-decode, catch-all reasoning) are non-applicable conditionals.
✅ Check 6 (upstream image / engine-first): PASS — vllm/vllm-openai-rocm:nightly-387189c… is the upstream vLLM org; framework vllm on established SKU MI355X. Engine-first N/A (vLLM entry).
✅ Check 7 (no arch hacks): PASS — no --hf-overrides; fp8 KV-cache, block-size, batched-tokens are serving config, not model-FLOPs reduction.
➖ Check 8 (spec-decode chat template): N/A — no speculative-decoding changes.
✅ Check 9 (no engine patch): PASS — env-var exports and serve args only; pip install amd-quark is a stock upstream quantization dependency, not an engine fork/patch.
➖ Check 10 (agentic golden AL): N/A — no agentic speculative-decoding changes.

❌❌❌ REJECTED ❌❌❌

@chunfangamd — blocking: this sign-off's Additional detail section links vllm-project/recipes#660, which documents the wrong model (amd/MiniMax-M3-MXFP4, models/MiniMaxAI/MiniMax-M3.yaml), not this PR's amd/Kimi-K2.5-MXFP4. The correct merged recipe #673 (models/moonshotai/Kimi-K2.5.yaml) is only linked in a later comment. Update this sign-off's Additional detail section to link #673, then re-run the gate.

✅ Check 0 (CODEOWNER): PASS — @chunfangamd owns configs/amd-master.yaml; the script + perf-changelog.yaml are catch-all (* @InferenceX/core), covered by any CODEOWNER.
✅ Check 1 (green in-PR sweep+eval): PASS — in-PR commit 45c6584 has executed, green single-node 8k1k / (TP4+TP8) and per-config eval / jobs in run 29953041481. (Head d1fd00e is a main-merge commit with all sweep jobs skipped; validation keys off any in-PR commit.)
✅ Check 2 (eval accuracy): PASS — GSM8K em_strict 96.89%–97.42% (n=1319) on amd/Kimi-K2.5-MXFP4, same image vllm/vllm-openai-rocm:nightly-387189c… as this PR's config.
❌ Check 3 (recipe merged+complete): FAIL — sign-off links #660 (MERGED, but documents amd/MiniMax-M3-MXFP4 with --tool-call-parser minimax_m3); model/model-path is a MAJOR arg and mismatches this PR's amd/Kimi-K2.5-MXFP4. The correct merged recipe #673 (Kimi-K2.5, merged 2026-07-23) is not linked in this sign-off — only in a separate comment. Link #673 in the Additional detail section.
✅ Check 4 (reuse command): PASS — /reuse-sweep-run posted by @chunfangamd (COLLABORATOR).
✅ Check 5 (latest template): PASS — all current-template items present; the two unchecked items (agentic spec-decode, catch-all reasoning) are non-applicable conditionals.
✅ Check 6 (upstream image / engine-first): PASS — vllm/vllm-openai-rocm:nightly-387189c… is the upstream vLLM org; framework: vllm, single-node established SKU MI355X. Engine-first N/A (vLLM entry).
✅ Check 7 (no arch hacks): PASS — no --hf-overrides; fp8 KV-cache / block-size / batched-tokens are serving config, not model-FLOPs reduction.
➖ Check 8 (spec-decode chat template): N/A — no speculative-decoding changes.
✅ Check 9 (no engine patch): PASS — env-var exports and serve args only; pip install amd-quark is a stock upstream quantization dependency, not an engine fork/patch.
➖ Check 10 (agentic golden AL): N/A — no agentic speculative-decoding changes.