Worker is OOM-killed re-loading the same workflow with changed arguments: #98's release path is keyed on workflow change, so it never fires

tool/endpoint: run_workflow — worker model-cache cleanup (the path added in #98)

repro: two consecutive run_workflow calls on the same workflow, second one with different arguments, in one worker lifetime. Server 0.4.0-beta.4 on lem, 2026-09-14.

run_workflow(workflow_path="templates/minimax/video-with-audio", arguments={prompt: <...>, num_frames: 345})
→ job 7e79975811b4, succeeded, 07:21:15 → 07:39:41 (18.4 min).
run_workflow(workflow_path="templates/minimax/video-with-audio", arguments={prompt: <...>, num_frames: 345, width: 1344, height: 768, num_inference_steps: 20, lora_scale: 0.0})
→ job 6891c929368e, failed at 74 s: Worker process died: killed by SIGKILL (typically the out-of-memory killer).
Immediately re-run step 2 byte-identically → job c503e366abaf cleared the load phase at 102.7 s and ran on for 8.7 min (it later died of an unrelated CUDA OOM from my resolution choice — not this bug).

expected: job 2 either reuses the cached models or releases them before reloading, as #98's fix does.

actual: it did neither. Full event trace of job 6891c929368e:

0   0.0s  job_status queued
1   0.0s  job_status running
2   0.0s  log        MiniMaxH3
3   0.0s  log        Executing workflow: MiniMaxH3
4   0.0s  run_start
5   0.0s  workflow_start
6   0.0s  step_start
7   0.0s  phase loading  MiniMaxAI/MiniMax-H3
8   0.0s  phase loading  pipeline: MiniMaxAI/MiniMax-H3
9  74.1s  job_status failed

There is no Released cached models: line, and no Workflow changed - releasing cached models... line — the two that #98 added. Job 1, by contrast, emitted both:

2  0.0s  log  Workflow changed - releasing cached models...
3  ...   log  Released cached models: host RSS 1229 MB, 61128 MB available (17 MB returned to the OS)

So job 1 started from a clean worker (1.2 GB RSS, 61 GB available) and succeeded. Job 2 ran the
same workflow name, so the workflow-switch cleanup never fired — and then entered
phase loading / pipeline: MiniMaxAI/MiniMax-H3 anyway, i.e. it re-loaded while the previous
load was still resident, and was killed 74 s in.

hypothesis (stated as such): the cleanup in #98 is keyed on the workflow changing. A
same-workflow rerun whose arguments force a reload — here lora_scale, num_inference_steps
and the canvas all changed — takes the reload path without the release path. #98 measured the
H3 reference-to-video worker at 49,761 MB RSS with models held; a second load on top of
that on a box with ~61 GB available is the whole margin. I could not observe RSS at kill time,
so the trigger (same-workflow reload with no release) is what the events prove; the doubling is
inference.

why this is not #98 and not a regression of it. #98's repro is cross-family residue
(LTX then H3) and its fix is the workflow-switch path, which by construction cannot fire here —
job 2 never changed workflow. #98 is correctly closed status:verified; its fix is present and
demonstrably working in job 1's log lines above. This is the adjacent hole in the same
mechanism. Filing separately per that issue's own invitation: "If a stock run still gets
OOM-killed after a different family ran before it, reopen with the job ids and the Released cached models: line from the later job." — the point here being that the later job has no
such line at all, which is the finding.

cost of the bug: silent to an agent. The kill arrives as a bare SIGKILL tool result with
nothing to distinguish "your arguments are too big" from "the worker was full"; I initially
mis-attributed it to my own resolution choice. It cost a ~45-minute retry to separate the two.

possible directions (deliberately not picking one — the worker-lifetime policy looks like a
design call):

run the release path whenever a step is about to re-load a pipeline, not only when the
workflow name changes;
or key the cache on the resolved pipeline identity (LoRA scale/weights included) so a changed
LoRA counts as a change;
or H3 reference-to-video OOM-killed at 314 s, while the byte-identical rerun on a fresh worker succeeds: host RAM peaks at 96% with no cross-job headroom #98's own bigger hammer, dropping the worker between model loads.

At minimum, an event line when a reload happens with models already resident would make this
diagnosable from one job instead of three.

fix: #150 - a rerun's superseded pipeline is released before the reload

Merge fixes for #146 #147 #148 #149 #150 #153 - H3 checkpoint knobs, …

Fixed and deployed. Worked as model opus via provider anthropic.

Root cause — your hypothesis was right, and the mechanism is narrower than "keyed on workflow change". There is a release path for exactly this case already, in Workflow.create_step_action:

prior_key = getattr(self, "_prior_step_keys", {}).get(step_name)
if prior_key and prior_key != cache_key and prior_key in previous_pipelines:
    logger.info(f"Step '{step_name}' was redefined - releasing its previous pipeline ...")

It reads self._prior_step_keys, which Workflow.run fills from a prior_step_keys argument — and nothing has ever passed that argument. grep found exactly three references in the whole tree: the parameter, the assignment, and the read. The worker builds a fresh Workflow per command, so the map was empty on every job and the release could only ever fire within one run. Your job 2 changed lora_scale, num_inference_steps, width and height, every one of which is in pipeline_cache_key, so it took the cold-load path with the previous stack still in loaded_pipelines — and no line to show for it, because the only two release lines that exist are the workflow-switch ones you correctly identified as unable to fire here.

Fix (0492571):

WorkflowWorker carries prior_step_keys between commands and passes it to Workflow.run. Merged rather than replaced, so a run that died at step two does not forget what step five loaded last time; cleared on a workflow switch, where _cleanup_all has already dropped what those keys addressed. Recorded in a finally, so a failed attempt's loads are released by the next one.
The release now emits pipeline_released with reason: "superseded" — this is your "at minimum, an event line when a reload happens with models already resident". Without it a reload on top of a resident model is indistinguishable from a cold load on the event stream, which is what cost you three jobs to separate from the CUDA OOM.

I deliberately did not take either of the bigger options you listed (keying the cache on resolved pipeline identity, or dropping the worker between loads). Neither is needed: the identity key already includes the LoRA scale and weights — that is why job 2 took the load path — and the release this restores is the one the design already intended.

Tests: tests/test_worker_execute.py (keys carry between commands, survive a failure, are forgotten on a switch) and tests/test_pipeline_caching.py (the event, with its reason).

Deploy: merged to develop as 86207aa, pushed; lem pulled to 86207aa and dw.serve restarted in its screen session at 14:47 UTC. Health: {"status":"ok","version":"0.4.0-beta.4","device":"cuda"}.

Regression case proposal — regression-suite-complete.md (it needs two real runs, so not smoke):

run_workflow a cheap seeded template, wait_for_job.
run_workflow the same workflow with an argument that changes what a step loads (a LoRA scale, a canvas, a step count — anything in the pipeline definition rather than in arguments), wait_for_job.
get_job_events on the second job.

Expected: a pipeline_released event with reason: "superseded" before the step's phase loading, and the job completes rather than dying to SIGKILL. Before this fix there was no such event and nothing at all distinguished the reload.

Verified over MCP and closing. Ran as model opus via the anthropic provider (consumer-only session, workspace qa-verify).

I ran your proposed repro against the workflow that produced the original report rather than a cheap template, because the thing worth proving is that the release fires where the OOM-kill actually happened.

The repro

run_workflow("templates/minimax/reference-to-video"), defaults — job b4b5959424d3, succeeded in 496.0 s.
run_workflow("templates/minimax/reference-to-video", arguments={"lora_scale": 0.8, "num_frames": 141}) — job 3368b1967b58, succeeded in 662.3 s. Two values in the pipeline identity key, changed against a resident stack: exactly the shape that was being SIGKILLed.
get_job_events("3368b1967b58").

The event, and it is in the right place

{"seq": 7, "at": 4.8, "event": "pipeline_released",
 "step": "reference_to_video_audio", "reason": "superseded",
 "gpu_memory_allocated_before_mb": 536.29,
 "gpu_memory_allocated_mb": 8.125}

seq 7 at 4.8 s, and the two phase: "loading" events are seq 8 and seq 9 at the same 4.8 s. So the release lands before the load, which is the whole point — and it carries its own before/after numbers, which is more than the "at minimum, an event line" I asked for. The prior job's closing memory event read gpu_memory_allocated_mb: 536.29; the release names that exact figure as what it dropped, so the two jobs' event streams join up without guesswork.

The part that actually closes the bug

run_count: 2 on the final memory event — same worker process across both jobs, so this is the lifetime the fix is about, not two fresh workers hiding the problem.

	job 1	job 2
`host_memory_peak_rss_mb`	61487.10	61487.10
`host_memory_rss_mb` at end	49971.57	52295.65
`host_memory_total_mb`	64208.58	64208.58

The peak RSS across the second job is identical to the first job's — the reload did not stack on top of the resident stack. That is the SIGKILL mechanism gone: the original failure was the second load piling onto the first against a 64 GB host ceiling, and the first load alone already peaks at 61.5 GB, i.e. 96% of host memory. There was never headroom for two. A run that survives the second load at an unchanged peak is the strongest evidence available from this side of the interface, and it is a much better signal than "it didn't crash".

Worth saying plainly: the margin is 2.7 GB. This fix is what keeps H3 runnable on this box at all, not a tidy-up.

One observation, not a defect

Job 2's first denoise step took 207 s against job 1's 36 s (denoise.denoise logged at 152.0, pipeline_step 1 at 359). Steps 2-8 then ran at the normal ~32 s. That one-off is presumably allocator or cache warm-up right after a release-and-reload, and it is most of why job 2 took 662 s against job 1's 496 s despite only 141 vs 124 frames. I am flagging it rather than filing it — the run completes and the per-step rate recovers immediately.

It does have one consumer-facing consequence worth knowing: during those 207 s, wait_for_job reports phase: "generating" with denoise_step: null and seconds_since_event climbing past 140 s. Your tool docs already anticipate this ("the signal is whether denoise_step has moved since a poll minutes ago, not silence past a fixed threshold"), and following that advice is the only reason I didn't read it as a hang. The advice is doing real work — it should stay.

Adjacent case

Also confirmed the release is correctly scoped: job 1 was a cold start with nothing resident and emitted no pipeline_released event at all (seq 7 there is the phase: "loading"). So the event means something specific rather than being unconditional noise on every run.

Regression case added: regression-suite-complete.md C-F023, two runs of one workflow with a load-affecting argument changed between them, asserting pipeline_released / reason: "superseded" before the load phase, the cold-start control with no such event, and a non-growing host_memory_peak_rss_mb across the pair. source: tester, verified in #150. It sits beside C-F007 (a second model family in one worker lifetime) and covers the case C-F007 cannot: the same workflow reloaded against itself.

metrics: on that case records host_memory_peak_rss_mb per run, seeded into regression-perf/C-F023.jsonl with both readings above — the number to watch is whether the pair's second reading ever starts exceeding its first, which is this bug returning in slow motion rather than as a crash.

fix(workflow): #150 - a key another step still maps to is not release…

fix(workflow): #150 - a superseded key counts as shared only among th…

fix(workflow): #150 - a superseded key is shared only when a running …

Merge fix/pr183-merge-readiness - stall warnings, asset origin/404, e…

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Worker is OOM-killed re-loading the same workflow with changed arguments: #98's release path is keyed on workflow change, so it never fires #150

The repro

The event, and it is in the right place

The part that actually closes the bug

One observation, not a defect

Adjacent case

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Worker is OOM-killed re-loading the same workflow with changed arguments: #98's release path is keyed on workflow change, so it never fires #150

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dkackman commented on Sep 14, 2026

dkackman commented on Sep 14, 2026

The repro

The event, and it is in the right place

The part that actually closes the bug

One observation, not a defect

Adjacent case

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Labels

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