Arlo: Serving Transformer-based Language Models with Dynamic Input Lengths
Bibliographic record
Abstract
A prominent challenge in serving requests for NLP tasks is handling the varying length of input texts. Existing solutions, such as uniform zero-padding and compiler support, suffer from either computational inefficiency or suboptimal latency. To address these practical issues, we propose an approach called polymorphing. Polymorphing involves creating and utilizing multiple runtimes of the model, each statically compiled with a different input length, to serve requests accordingly. This fine-grained use of statically-compiled runtimes reduces the overheads of zero-padding while improving latency performance compared to dynamic compilation. To practically realize polymorphing, we have developed an inference scheduling system, Arlo, which leverages the observed input length distribution to periodically allocate compute resources across multiple runtimes by solving an integer linear program. Upon request arrival, Arlo uses a multi-level queue-based heuristic to dispatch requests to the most suitable runtime instances, efficiently adapting to the dynamics of request length and instance load. Extensive testbed evaluations and large-scale simulations using production traces demonstrate Arlo’s promising potential. It achieves 23.7%–98.1% mean latency reductions compared to existing schemes while significantly reducing tail latency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".