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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34559632177 |
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34565384525 |
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Previous sweep reuse authorization withdrawn: the user requested adding TEP8/TEP8 points and running a new full official sweep. Run 34565384525 remains historical evidence for the earlier four-point matrix. |
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Cursor Bugbot has reviewed your changes using default effort and found 1 potential issue.
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Reviewed by Cursor Bugbot for commit eae1874. Configure here.
| if backend not in {"sglang", "dynamo"}: | ||
| raise SystemExit(f"Unsupported AIPERF_DRAIN_BACKEND: {backend}") | ||
| if not all(worker_urls): | ||
| raise SystemExit("Agentic drain requires non-empty worker metrics URLs") |
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Drain requires worker URLs unconditionally
Medium Severity
wait_for_agentic_servers_idle now treats an empty AIPERF_SERVER_METRICS_URLS as a fatal SystemExit for every backend. Existing Dynamo AgentX recipes that rely on this script never set that variable and previously drained from the frontend gauge alone. Because SystemExit bypasses the retry handler, any multi-concurrency job exits as soon as drain runs.
Reviewed by Cursor Bugbot for commit eae1874. Configure here.
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/stage-results 34565384525 |
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@cquil11 staged run 34565384525: https://inferencemax-app-git-staging-semianalysisai.vercel.app/inference?i_dates=2026-09-11~r34565384525 This run remains available across future |
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Nothing blocking. The comments below are optional suggestions. There is no need to push a fix for them before merging.
Beyond the inline findings, I also checked utils/srt_slurm.py's image-cache handling: prepare_recipe only reads a pre-existing cached image path under SRT_SLURM_SHARED_BASE/containers and falls back to the raw image tag when no cached artifact exists — it never writes to or grows that directory itself, so this PR isn't introducing new unbounded cache growth outside GITHUB_WORKSPACE.
Extended reasoning...
This run reported findings (including a confirmed WORK_DIR-not-cleaned-up bug in runners/launch_srt_slurm.sh) that will be posted as inline comments, so approval is not appropriate. Beyond those, I independently checked utils/srt_slurm.py's prepare_recipe to see whether its image-cache lookup (image_cache / (image.replace(...) + '.sqsh'), backing --image-cache SRT_SLURM_SHARED_BASE/containers in runners/launch_srt_slurm.sh) itself creates or grows a persistent cache directory outside the CI workspace's clean lifecycle. It does not: the function only performs a read-only is_file() check and falls back to the plain image tag if no cached artifact is present, so this PR's own code path does not add to any cache-growth concern for that directory.
| SRT_SLURM_REPOSITORY="${SRT_SLURM_REPOSITORY:-https://github.com/SemiAnalysisAI/srt-slurm.git}" | ||
| SRT_SLURM_COMMIT="${SRT_SLURM_COMMIT:-81d46274f508e18ab14d1f123b75132005818dcf}" | ||
| RUN_KEY="${GITHUB_RUN_ID:-manual}-${GITHUB_RUN_ATTEMPT:-0}-${RUNNER_NAME:-runner}" | ||
| WORK_DIR=$(mktemp -d "${GITHUB_WORKSPACE}/.srt-slurm-${RUN_KEY}.XXXXXX") |
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🟡 (optional) WORK_DIR is created with mktemp under $GITHUB_WORKSPACE per invocation (unique per RUN_KEY) but is never removed on any exit path, including the success path and the cleanup() trap, unlike every other srt-slurm launcher in this repo (e.g. launch_b200-nscale-slurm.sh) which reuses a fixed dir and rm -rf's it before/after cloning specifically because GITHUB_WORKSPACE persists across jobs on these self-hosted runners. Each run leaves behind a full srt-slurm git clone plus its uv venv and outputs, so repeated CI runs (this is a full-sweep-enabled, repeatedly-scheduled workflow) accumulate GBs of clones/venvs on the shared runner disk indefinitely. Fix: rm -rf "$WORK_DIR" in cleanup() (or trap it separately) after collect_results has copied out what it needs, regardless of exit code.
Extended reasoning...
launch_mi300x-amd.sh execs launch_srt_slurm.sh whenever CONFIG_FILE is set (i.e. for every srt-slurm recipe run on this fleet). WORK_DIR=$(mktemp -d "$GITHUB_WORKSPACE/.srt-slurm-${RUN_KEY}.XXXXXX") creates a fresh directory named with GITHUB_RUN_ID-GITHUB_RUN_ATTEMPT-RUNNER_NAME, so it never collides with a prior run's directory (unlike the fixed 'srt-slurm' dir other launchers rm -rf before cloning into). git clone + make setup-compute + uv venv + uv pip install -e . populate SRT_REPO_DIR=$WORK_DIR/srt-slurm, and profile output_dir/results also live under $WORK_DIR. cleanup() (the EXIT trap) only calls the python adapter's collect (which copies files OUT of $WORK_DIR/results_root into $GITHUB_WORKSPACE) and then exit "$rc"; nothing in the file ever deletes $WORK_DIR itself. Because GITHUB_WORKSPACE is a persistent path on this self-hosted fleet (confirmed by sibling launchers explicitly rm -rf "$SRT_REPO_DIR" before every clone to avoid exactly this buildup), every successful or failed run of this new script leaves its full clone+venv on disk. Over the many scheduled…
Verification: nit. Factual claim is correct: launch_srt_slurm.sh:17 creates WORK_DIR=$(mktemp -d "${GITHUB_WORKSPACE}/.srt-slurm-${RUN_KEY}.XXXXXX") with a per-run-unique name (RUN_KEY=GITHUB_RUN_ID-RUN_ATTEMPT-RUNNER_NAME plus mktemp's .XXXXXX), and nothing in the script ever removes it. The cleanup() trap (lines 46-60) only scancels the job and runs the collect adapter; it explicitly does `trap - EXIT INT…
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see unofficial run visualizer at https://inferencex.semianalysis.com/inference?unofficialRun=34619026982 |
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/reuse-sweep-run 34619026982 |
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/stage-results 34619026982 |
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@cquil11 staged run 34619026982: https://inferencemax-app-git-staging-semianalysisai.vercel.app/inference?i_dates=2026-09-11~r34619026982 This run remains available across future |
1 participant
Scope
Qwen3.5-397B-A17B FP8 AgentX disaggregation on MI300X using native MORI and srt-slurm. Based on main; no unrelated MI355X configurations or workflow changes.
lmsysorg/sglang-rocm:v0.5.19-rocm720-mi30x-20260910, digest pinned in each recipe.c6dc2f05061e504c59b8e7baa30cfeacabc93645, owns allocation, containers, router, workers, and benchmark lifecycle.Correctness blocker for requested mixed layout
TEP8 prefill to DEP8 decode changes attention TP8 to TP1. The pinned SGLang MORI transfer concatenates each local packed Q/K/V convolution-state shard instead of assembling each channel group separately. This silently rearranges Qwen recurrent state. The mixed layout is excluded pending a stock upstream fix.
The exact pinned transfer method was exercised on CPU with in-bounds byte-copy transport and actual model dimensions: 11,008 of 12,288 channels land incorrectly. Transfer implementation, grouped state definition.
Validation
no-evals: trueoption.full-sweep-enabledmatrix contains eight performance jobs, default warmup and 3600-second profiles, with no eval jobs. Fresh eight-point Run Sweep completed successfully at7267a73c36d4a5d03f86a8bd79f32fbf0650ee16: all eight actual GPU jobs passed, along with the changelog check and final collection. Prior sweep reuse authorization was withdrawn before launch; these are fresh measurements.6da8c6f4695a57670b4ee8048373a94ce74933d1; all four performance jobs passed.agent/mi300x-qwen35-agentx-mori-frontier; the broad sweep was canceled at the user's request to narrow topology search.Fresh eight-point official performance results
All points use 16 physical GPUs. Interactivity below is P90 E2E-normalized output tokens/s/user; both displayed performance axes are higher-is-better. Values are from the official benchmark artifacts.
The DEP points are not performant: P90 time to first token is 284.58 seconds at c32 and 802.78 seconds at c64. They incurred 39 and 87 end-of-window cancellations respectively. The c64 artifact also records one
ClientPayloadErrorbefore mass cancellation, with an approximately 1800-second request lifetime and a matching upstream stream termination. This repeats the prior c64 timeout pattern; it is not a cutoff cancellation or a demonstrated RDMA fault. The other seven points record zero client errors. TEP8 c8/c16 had two/one end-of-window cancellations respectively; both pure-TP points and TEP8 c1/c4 had none.TEP8 c16 is the highest-throughput measured point. Pure TP8 at c1/c4 exceeds the matching TEP8 low-concurrency points on both displayed metrics. CI success does not establish model accuracy or a competitive frontier against matched full-length single-node controls.