TreeWalker: Partial Evaluation for Grouped Tree-Ensemble Inference

arXiv:2610.03939v1 Announce Type: new Abstract: Many inference workloads evaluate a trained tree ensemble on row groups that share feature values: discrete-time survival models expand each patient into $G$ time steps, click-through-rate models score every item in a search session, and scenario analyses vary a few inputs while holding the rest fixed. Standard inference treats each row independently and repeats the shared work $G$ times. We present TreeWalker, which applies partial evaluation to grouped inference: constant features are static, varying features dynamic. It walks each tree once per group, partitions a row bitmask at varying splits, and skips empty subtrees. Training is unchanged: TreeWalker reads standard LightGBM and XGBoost models. We prove a structural work decomposition: per-tree work splits into the constant-projected subtree size $|T_c|$, $G$ leaf writes, and a predicate-mask provisioning cost $Q$. For the trace evaluator, per-row work approaches a $(d_v+1)/(d+1)$ fraction of a row-independent walk as $G \to \infty$. On Intel, TreeWalker is 2.5-3.2$\times$ faster than a row-independent traversal at the reference configuration ($T=500$, $L=8$) and 6.8-7.8$\times$ faster at $G=128$ on the survival datasets, with larger gains on Arm. On a scenario-analysis benchmark it is faster in all 16 configurations on both architectures. For f64 models, outputs match treelite's GTIL up to summation order; for f32 models, f64 accumulation is closer to a Kahan-compensated reference than native f32 on 99.98% of rows and never farther.

Abstract:Many inference workloads evaluate a trained tree ensemble on row groups that share feature values: discrete-time survival models expand each patient into $G$ time steps, click-through-rate models score every item in a search session, and scenario analyses vary a few inputs while holding the rest fixed. Standard inference treats each row independently and repeats the shared work $G$ times. We present TreeWalker, which applies partial evaluation to grouped inference: constant features are static, varying features dynamic. It walks each tree once per group, partitions a row bitmask at varying splits, and skips empty subtrees. Training is unchanged: TreeWalker reads standard LightGBM and XGBoost models. We prove a structural work decomposition: per-tree work splits into the constant-projected subtree size $|T_c|$, $G$ leaf writes, and a predicate-mask provisioning cost $Q$. For the trace evaluator, per-row work approaches a $(d_v+1)/(d+1)$ fraction of a row-independent walk as $G \to \infty$. On Intel, TreeWalker is 2.5-3.2$\times$ faster than a row-independent traversal at the reference configuration ($T=500$, $L=8$) and 6.8-7.8$\times$ faster at $G=128$ on the survival datasets, with larger gains on Arm. On a scenario-analysis benchmark it is faster in all 16 configurations on both architectures. For f64 models, outputs match treelite's GTIL up to summation order; for f32 models, f64 accumulation is closer to a Kahan-compensated reference than native f32 on 99.98% of rows and never farther.

23 pages, 9 figures, 11 tables. Accepted at NeurIPS 2026. Code and data: this https URL

Machine Learning (cs.LG); Performance (cs.PF); Programming Languages (cs.PL)

arXiv:2610.03939 [cs.LG]

(or arXiv:2610.03939v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.03939 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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TreeWalker: Partial Evaluation for Grouped Tree-Ensemble Inference

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