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fix(planner): prevent ARIMA forecasts from locking at zero - #18152
Summary: Prevent nearly constant positive request-length observations from locking ARIMA forecasts at zero and making Planner throughput capacity unavailable. Keep valid zero-demand forecasts unchanged and recover a previously fitted variance-only model without mixing raw and log-space history.
Key Changes
28 added core-logic lines, excluding tests, comments, and whitespace; no new dependencies or configuration fields.
predictors.py: recognize numerical near-constants in raw and transformed space before fitting, and preserve valid intercept-bearing raw mean models.
predictors.py: use the latest observation when a variance-only model predicts zero for positive input, then reconstruct the next fit from canonical raw history.
test_load_predictors.py: cover RAW/LOG1P initialization, meaningful variation after a near-constant prefix, valid mean-model retention, poisoned-model recovery, and genuine idle/tiny-positive values.
Validation
The triggering sequence differs by one floating-point ULP. The raw equality guard previously missed it; log1p collapsed it to a constant, and pmdarima fitted a zero-mean ARIMA(0,0,0) with only a variance parameter. Incremental updates cannot change that mean.
Behavior
Before
After
Nearly constant OSL followed by varied positive values, RAW and LOG1P
Can initialize a model that forecasts zero indefinitely
Remains near the observed length and begins fitting when meaningful variation arrives
Numerically constant transformed history
Calls the library's constant-series fit path
Returns the original-scale observation without fitting
Raw ARIMA(0,0,0) with an intercept
Unnecessarily replaced by a log-space model
Retained as a valid mean forecast
Existing zero-mean model with positive observations
Remains at zero
Falls back to the latest value, then refits from correctly reconstructed history
Genuine idle and tiny-positive observations
Existing zero/positive semantics
Preserved; no arbitrary positive forecast floor
Seven new regression cases fail against unmodified main. The fixed predictor, warmup, runtime-reconfiguration, and simulation-predictor suites pass all 98 cases. The slowest new case took approximately 0.9 seconds locally; the others were below 0.4 seconds. CPU-only validation used pmdarima 2.1.1 with the pinned AISimulate dependency available. No serving-performance claim or GPU experiment is part of this PR.
Searched main, open PRs, and issues for ARIMA/zero-forecast fixes; no matching fix found.
Related #14238 concerns predictor/default benchmarking, not this constant-series correctness defect. This PR does not change the default predictor or tune forecasting parameters.
Pull Request Checklist
I used Dynamo's /pr-checklist skill.
Summary by CodeRabbit
Bug Fixes
Improved load forecasts for constant or nearly constant histories, including zero and very small values, by keeping forecasts aligned with the latest observation.
Improved handling of changes from steady to variable usage in both prediction modes, and recovery when a forecast conflicts with a positive recent observation.
The ARIMA predictor now detects constant raw and log1p histories, adjusts raw-to-log1p fallback selection, and handles a specified zero-forecast case. Unit tests cover near-constant histories, subsequent variation, raw-mode mean behavior, and poisoned log-space models.
Changes
ARIMA Predictor
Layer / File(s)
Summary
Constant history handling components/src/dynamo/planner/core/load/predictors.py, components/src/dynamo/planner/tests/unit/test_load_predictors.py
The predictor returns the latest raw value when raw or log1p history is constant. On initial fitting, it rebuilds the buffer in the selected mode. Tests cover near-constant and repeated values, large values with tiny increments, and later variation.
Fallback and zero-forecast recovery components/src/dynamo/planner/core/load/predictors.py, components/src/dynamo/planner/tests/unit/test_load_predictors.py
The raw-to-log1p fallback excludes the specified intercept mean model. When the specified non-seasonal model forecasts zero for a positive latest value, the predictor clears the model and returns that value. Tests cover raw-mode mean behavior and poisoned log-space models.
The reviewed changes have no identified merge-blocking risk and are ready for normal merge checks.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 14.29% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
Status
Explanation
Title check
The title clearly and concisely describes the main change: preventing ARIMA forecasts from remaining at zero.
Description check
The description includes all required sections: Overview, Key Changes, Validation, and Issues and Ongoing Work. It explains the problem, implementation, tests, validation results, issue link, and chec…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Description check
Explanation
The description includes all required sections: Overview, Key Changes, Validation, and Issues and Ongoing Work. It explains the problem, implementation, tests, validation results, issue link, and checklist status.
Full details: Docstring Coverage
Explanation
Docstring coverage is 14.29% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 7 functions across 2 files.
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Autofix · Keep fixing CodeRabbit findings and required CI, and resolving merge conflicts
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Prompt to fix all issues
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only still-valid issues, skip the rest with a brief reason, keep changes
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Failed pre-merge checks:
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Write docstrings for the functions missing them to satisfy the coverage
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Fourth upstream merge for the split v2 PR: the five commits past
02331f0. Four conflicts, all from ai-dynamo#18009, placed in the owner crates;
upstream behavior whole, the stack's moves intact, no shims.
- ai-dynamo#18009 keep prepared rows when ranks are past the reject thresholds:
is_worker_full(dp_size)/skips_full_workers in the kernel's filter.rs;
the actor keeps prepared rows and the kernel's select_with_policy drops
the rows of workers whose every rank is full before the policy adopts
them (PreparedRows::retain in worker-selection; the policy recounts the
best cached prefix from the rows it adopts and reports whether any is
left); prepared rows carry dp_size (u16, so the 128-byte row holds) set
in the kernel's CandidatePreparerExt::prepare; the three new kernel
queue tests and the relocated test helpers updated.
- ai-dynamo#18108 CRTC decode stores appended at the leaf tail under one lock:
lib/router/kv-indexer concurrent_radix_tree_compressed (auto-merged).
- ai-dynamo#16258 vLLM backend defaults for omitted token limits: lib/llm
preprocessor, lib/sidecar/vllm, components, docs, tests (auto-merged).
- ai-dynamo#18152 planner ARIMA forecasts, ai-dynamo#16426 operator crash-loop: outside the
router (auto-merged).
Signed-off-by: Ishan Dhanani <ishandhanani@gmail.com>
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Overview
Summary: Prevent nearly constant positive request-length observations from locking ARIMA forecasts at zero and making Planner throughput capacity unavailable. Keep valid zero-demand forecasts unchanged and recover a previously fitted variance-only model without mixing raw and log-space history.
Key Changes
28 added core-logic lines, excluding tests, comments, and whitespace; no new dependencies or configuration fields.
Validation
The triggering sequence differs by one floating-point ULP. The raw equality guard previously missed it; log1p collapsed it to a constant, and pmdarima fitted a zero-mean ARIMA(0,0,0) with only a variance parameter. Incremental updates cannot change that mean.
Seven new regression cases fail against unmodified main. The fixed predictor, warmup, runtime-reconfiguration, and simulation-predictor suites pass all 98 cases. The slowest new case took approximately 0.9 seconds locally; the others were below 0.4 seconds. CPU-only validation used pmdarima 2.1.1 with the pinned AISimulate dependency available. No serving-performance claim or GPU experiment is part of this PR.
Issues and Ongoing Work
Pull Request Checklist
/pr-checklistskill.Summary by CodeRabbit