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The nightly lane has failed every night since 09-04. It ran every HeavyTimeout
model in ONE test host, and that host's memory only grew: serialized execution
stops the models overlapping but does not return a model's weights to the OS
between classes. The 16 GB hosted runner was exhausted partway through and the
OOM killer took the runner agent, which GitHub reports as "The runner has
received a shutdown signal" rather than as a test failure. continue-on-error
cannot catch that, because a dead runner is not a failed step.
It is cumulative, not one bad model. Where it died on five consecutive nights:
09-09 Mask2Former 19 min in
09-08 MoMask 9 min in
09-06 CogView4 14 min in
09-05 SDXLTurbo 9 min in
Different model every night, always a few minutes after the previous test
passed, and nowhere near the 350-minute job budget.
Now runs in chunks of 6 classes, each in its own `dotnet test` process, so peak
memory is bounded by a chunk rather than by the whole category, and a chunk that
still exhausts the runner costs only its own results instead of every class
after it. Chunk size is a workflow_dispatch input for bisecting the ceiling.
Chunks rather than one process per class, because this lane is far larger than
anything written down about it: 219 test classes and ~3,800 tests, not the "20
traits across 14 files" the issue describes or the "11 tests" the comment in
this workflow claimed. The generator's HeavyTimeoutTestClassNames set has grown
to include the whole Diffusion family. VSTest discovers the entire ~72k-case
assembly on every invocation regardless of --filter, so 219 invocations would
spend the budget re-discovering.
The class list is discovered from --list-tests rather than written here, since
HeavyTimeout comes from both hand-written [Trait] attributes and the generator's
set, and any list committed here would drift from the generator immediately.
Discovery returning nothing is a hard failure: a lane that runs zero tests and
reports success is the same silent zero-coverage this issue is about.
Adds MemAvailable before and after each chunk plus a job-summary table, so the
next runner death arrives with evidence instead of only a shutdown signal.
Refs #2087, #1706, #1714
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Included review availability: Your plan provides up to 1 included review per hour; 0 remain after this review. WalkthroughThe workflow adds configurable heavy-timeout test chunking. The generator adds reduced CI-smoke fixtures. Transformer NER enables default warmup and validates zero-rate preparation. Configuration cloning and generated-fixture regression tests are expanded. ChangesCI and NER conformance updates
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~60 minutes Change: Bug fix Sequence Diagram(s)sequenceDiagram
participant NightlyWorkflow
participant ChunkedRunner
participant DotnetTest
participant StepSummary
NightlyWorkflow->>ChunkedRunner: Pass chunk_size
ChunkedRunner->>DotnetTest: Discover HeavyTimeout classes
DotnetTest-->>ChunkedRunner: Return class names
ChunkedRunner->>DotnetTest: Run each class chunk
DotnetTest-->>ChunkedRunner: Return exit code and metrics
ChunkedRunner->>StepSummary: Append chunk results
Merge Risk: ⚪ Minimal · up to No actionable merge-blocking risk remains in the reviewed changes. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 12.68% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 71 functions across 11 files. (2 skipped: 1 unsupported, 1 too large.) ✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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In @.github/workflows/heavy-timeout-nightly.yml:
- Line 133: Validate HEAVY_CHUNK_SIZE before assigning it to $chunkSize in the
chunking loop: accept only numeric positive values, retain the default when
unset, and fail clearly for zero, negative, or non-numeric input. Ensure the
validated value is always a positive loop step so the iteration advances and
existing chunk processing remains unchanged.
- Line 227: Update the workflow step containing the discovery guard and
aggregated $failed check so continue-on-error no longer masks their exit
statuses; preserve tolerance for individual chunk results while ensuring empty
discovery and chunk failures produce the intended job-level failure.
- Around line 154-159: Wrap the pipeline assigned to $classes in an array
subexpression so it remains an array even when discovery returns a single class.
Preserve the existing filtering, normalization, and sorting steps, ensuring
later range indexing operates on class names rather than characters.
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Adversarial follow-up completed against current Resolved root-level failure-reporting gaps:
Local verification on the updated branch:
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…tep monotonicity LegalBERTNER failed MoreData_ShouldNotDegrade every night: its loss rose from 2.825676 after one optimizer step to 3.992393 after two. Two separate defects were behind it. 1. Transformer NER models had no warmup. TransformerNEROptions.WarmupSteps defaulted to 0, so CreateDefaultOptimizer's `WarmupSteps > 0 || TotalTrainingSteps > 0` guard never fired and every model in this family took raw AdamW steps straight from initialisation -- the condition BERT-family fine-tuning uses warmup to avoid. Its own sibling PromptNER already defaulted to a warmup of ten through CreatePaperOptimizer; the shared base did not. Default is now ten, and zero still disables it, so the documented opt-out is unchanged. Warmup also has to start above zero. WarmupInitialLearningRate defaults to 0, and a linear ramp from 0 spends its first update not moving at all, which would have broken Training_ShouldChangeParameters across the family. It now starts at one warmup-step's worth of the target rate -- again what PromptNER already does -- and an explicitly configured value is still honoured. 2. The NER test override asserted a property SGD does not have. NERModelTestBase.MoreData_ShouldNotDegrade compared a short run against a longer one. NeuralNetworkModelTestBase had already abandoned that shape, with the optical-flow family as evidence: SEA-RAFT went 0.404 untrained, 38.6, 100.2, 1.26, ..., 0.111 by step 15, so step 1 against step 2 read 38.6 against 100.2 and called a model that ends 3.6x BETTER than untrained a regression. The override kept the comparison while fixing the different thing it actually needed -- NER Predict argmax-decodes, so it measures GetLastLoss instead. That part is preserved; the comparison is not. It now takes a baseline after the first step and compares once, after a budget resolved by the base's own ResolveConformanceTrainingIterations, which scales the work to the model's parameter count rather than a number invented in the test. The LegalBERTNER-only MoreDataTolerance of 1.0 is gone with it. That bound was holding the old shape up, and it was already stale: measured at a 0.608 gap when written, 1.167 in the nightly that finally exceeded it. A per-model bound tracking a value that moves with initialisation is the symptom, not the fix. Verified in Release across all 13 transformer NER models: 479 passed, and LegalBERTNER's MoreData_ShouldNotDegrade now passes inside the one-step conformance budget -- with warmup the second step no longer overshoots, so no large budget is needed. Training_ShouldChangeParameters passes everywhere, which is the assertion the zero-rate first step would have broken. NOT fixed here, and pre-existing: LegalBERTNER's Training_ShouldReduceLoss sits right on its 120s timeout. It runs 31 optimizer steps of a BERT encoder, and 31 steps measured 114s for this model when the budget was raised to observe it, so it has always been ~6s under the limit and passes or fails with machine load. This change does not alter that test's step count, and a warmup schedule adds one O(1) update per batch. It needs its own timeout or iteration decision, alongside the HeavyTimeout retag in #1714. Closes #2135 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016jgqTmscEnkgmAp1TkNFpG
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… missing Training_ShouldReduceLoss ran 31 optimizer steps of a full BERT-base encoder against a 120s gate. Measured at 114s, so it passed or failed with machine load rather than with the code. The escalation for this is float, then iteration caps, then a CI-smoke fixture. Rungs 1 and 2 were already applied -- LegalBERTNER is in Fp32TestClassNames and gets the NER family's smoke-iteration caps -- and the HeavyTimeout note recorded that they were not enough: "still OOM or time out on the forward/training even at <float>", followed by "A CI-smoke constructorExpr could rescue several, but that per-model work is deferred". This is that deferred work, and it is why the model was left running at full scale in the nightly lane. The fixture mirrors FinBERTNER, its sibling on the same TransformerNERBase, whose comment describes the identical problem: without one it inherits the paper shape (768 hidden, 12 encoder blocks, 3072 FFN, padding to 256 tokens) to label an eight-token generated task. Same encoder and token-classification head at smoke scale, AdamW still at the paper's 5e-5. Production defaults are untouched. A constructor is only half of it. The generated InputShape must be narrowed to match, or a 32-wide encoder is fed the paper-width [8, 768] and every forward throws "embedding dimension (768) does not match weight dimension (32)" inside MultiHeadAttention -- 32 failures where there had been one timeout. The file says so directly, "keep this list in sync with the HiddenDimension = 32 constructorExpr branches", and this commit adds LegalBERTNER to both. Verified in Release: the whole class is 37 passed / 0 failed in 46 seconds, with Training_ShouldReduceLoss and MoreData_ShouldNotDegrade both inside the gate. Refs #2087, #2135, #1714 Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016jgqTmscEnkgmAp1TkNFpG
…epLab and MelGAN These three sit with LegalBERTNER in the HeavyTimeout group whose note reads "A CI-smoke constructorExpr could rescue several, but that per-model work is deferred; for now they run at full scale in the nightly HeavyTimeout lane". Each had the earlier rungs applied and recorded as insufficient, so the fixture was the only lever left. This is that deferred work for the rest of the group. Each mirrors its own family's existing fixture rather than one shared shape, because the pairing between a shrunk model and its generated input is family-specific and getting it wrong is silent: MemFlow (OpticalFlowBase, after RAFT) 256x256 -> 64x64, features 64 -> 8, layers 8 -> 2 KMaXDeepLab (PanopticSeg, after Mask2Former) 640x640 -> 128x128, classes 133 -> 4 MelGAN (VocoderBase, after Vocos) NgfBase 512 -> 32 Three per-model details that do not transfer between them: MemFlow keeps InputDepth 6. RAFT uses 3, but MemFlow stacks the two frames channel-wise, the lazy feature conv is sized from InputDepth, and PredictCore splits on Shape[1]/2 -- copying RAFT's 3 would build a single-frame extractor and halve the wrong axis. KMaXDeepLab's real lever is numClasses, not resolution: the base derives its stuff/thing split from numClasses/3 and the mask decoder's query set scales with it. ModelSize stays R50, already the smaller of the two available. MelGAN may only move NgfBase. InitializeLayers builds CreateDefaultHiFiGANLayers(MelChannels, NgfBase, 1) and guards the options it does not consume: an earlier draft here set NumResStacks = 1 and failed all 33 tests on "configured but not applied by the paper-faithful HiFi-GAN generator default". The guard is correct -- silently ignoring a configured option is worse than refusing it. MelChannels is applied and would shrink further, but it is the 80 in this model's declared [1,80,8] -> [1,1,2048] contract, so moving it would change the output axes rather than only the cost. Verified in Release, each with its family's precedent run alongside as a control so a harness problem could not be mistaken for a fixture problem: MemFlow 34 passed / 0 failed / 1m53s KMaXDeepLab passed (control Mask2Former passed) MelGAN 66 passed / 0 failed / 2m48s (control Vocos passed) Production defaults are untouched and remain user-selectable in every case. Refs #2087, #1714 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016jgqTmscEnkgmAp1TkNFpG
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Inline comments:
In `@src/AiDotNet.Generators/TestScaffoldGenerator.cs`:
- Around line 11252-11274: Add "MemFlow" to the exclusion tuple in the generic
parameterless-constructor fallback so the later MemFlow-specific constructor
branch is reachable. Preserve the existing exclusions and ensure MemFlow uses
its reduced-scale constructor configuration rather than the generic
parameterless constructor.
- Around line 8871-8901: The MelGAN fixture comment documents the wrong tensor
shape. Update the MelGAN branch around the constructor expression to state the
shared shape logic’s actual contract, [1,80,1] input to [1,1,256] output; do not
add a shape override unless this fixture is explicitly intended to provide
eight-frame coverage.
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These write-ups should never have been committed. Removed here so the file does not arrive on master when this PR merges; .gitignore gains matching rules in #2224. Deliberately untouched: ci-proof/nonruntime-routing-canary.md, which is functional rather than a write-up (it exercises the permanent ci-proof/** workflow trigger). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01GYQycGHKxLFBqhEtPMHz29
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Review follow-up and verified result
Fixes the two remaining scaffold review findings (MemFlow construction and the MelGAN shape comment), and the shared NER/ONNX defects exposed during full-family verification.
Local proof on the final source
The combined NER run took 19.9154 minutes and observed a peak working set of 8.8249 GiB. The 29 skips comprise 28 opt-in census tests and one existing Biaffine span-target skip. This is actual combined-run evidence, not a sum of isolated runs.
Failure-first results, assembly hashes, independent checks, reproduction commands and limitations.
These are local CPU results, not a hosted full-nightly result or a GPU performance claim. Process chunking limits cross-process retained state but cannot guarantee recovery from a runner shutdown. A shutdown signal alone does not establish OOM causality. Hosted checks must be assessed on this pushed head.
Original PR narrative (historical observations and hypotheses; superseded by the measured proof above)
Fixes the runner death in #2087.
Root cause
The lane ran every HeavyTimeout model in one test host. Serialized execution stops the models overlapping but does not return a model's weights to the OS between classes, so the host's memory only grew until the 16 GB hosted runner was exhausted and the OOM killer took the runner agent with it.
GitHub reports that as
The runner has received a shutdown signal, not as a test failure — andcontinue-on-error: truecannot catch it, because a dead runner is not a failed step. That is why five consecutive nights were red with nothing actionable in the log.It is cumulative, not one bad model. Where it died on five consecutive nights:
SegmentationTrainingRobustness.Mask2Former_…MoMaskModelTests.ScaledInput_ShouldChangeOutputCogView4ModelTests.Scheduler_ShouldBeNonNullSDXLTurboModelTests.Predict_ShouldBeDeterministicA different model every night, always a few minutes after the previous test passed, and nowhere near the 350-minute job budget. That is a ceiling being reached, not a specific test crashing.
The lane is ~200x larger than anything written down about it
Measured on master via
--list-tests --filter Category=HeavyTimeout:[Trait]attributes across 14 test files"Both are stale. HeavyTimeout comes from two sources — hand-written
[Trait("Category","HeavyTimeout")]attributes (still 20) andTestScaffoldGenerator.HeavyTimeoutTestClassNames, which has grown to include essentially the whole Diffusion family. Nobody adding a model to that set was changing a 14-file lane; they were adding to a 219-class one.This is worth its own decision, and it is the data #1714 (retag to the real timeout list) has been blocked on. Not addressed here.
The change
Run in chunks of 6 classes, each in its own
dotnet testprocess. Peak memory is bounded by a chunk instead of the whole category, and a chunk that still exhausts the runner costs only its own results rather than every class after it.Chunks rather than one process per class because VSTest discovers the entire ~72k-case assembly on every invocation regardless of
--filter, so 219 invocations would spend the budget re-discovering. Chunk size is aworkflow_dispatchinput so the ceiling can be bisected without editing the workflow.The class list is discovered, not committed here — a hardcoded list would drift from the generator's set the first time a model was added to it. Discovery returning nothing is a hard failure with the tail of the output dumped, because a lane that runs zero tests and reports success is the same silent zero-coverage this issue is about.
Adds
MemAvailablebefore and after each chunk plus a job-summary table, so the next runner death arrives with evidence rather than only a shutdown signal.Verified before pushing
A nightly-only workflow is expensive to debug by trial, so the mechanical parts were checked locally rather than on the runner:
--list-testsoutput from this repo: 3,835 test lines → 219 distinct classes. Confirmed the two things the regex depends on — test lines are indented exactly 4 spaces and CRLF-terminated, while xUnit's interleaved diagnostic lines start at column 0 and are correctly excludedTheory_Case(variant: "a.b")) by splitting on(before taking the last segmentWhat this does not do
LegalBERTNERTests.MoreData_ShouldNotDegradegenuinely failed —2-iteration clone loss (3.992393) > 1-iteration loss (2.825676). That is a live optimizer-divergence signal that has been hidden behind the OOM, and it means the lane will still be red after this fix until it is addressed. Arguably the most valuable thing the outage was concealing.Summary by CodeRabbit
New Features
Bug Fixes