コンパイラ世界モデルに向けて：効率的なテンソルプログラム探索のための潜在ダイナミクス学習
Toward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search
テンソルプログラム最適化の探索コストを削減するため、スケジュール評価を行動条件付きの潜在ダイナミクスとしてモデル化する世界モデル風評価器を提案し、TVM AutoSchedulerでAnsorを上回る性能を達成した。
著者: Haolin Pan, Lianghong Huang, Xvlin Zhou, Mingjie Xing, Yanjun Wu
分類: cs.LG, cs.PL
原文アブストラクト
Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurement cost with learned cost models, yet they usually evaluate each candidate as a static code snapshot, ignoring the schedule trajectory that produced it. This makes them insensitive to action dependencies and vulnerable to superficial code variations. We propose a \emph{world-model-inspired} evaluator that models schedule evaluation as action-conditioned latent dynamics over program states. Starting from the initial program, it rolls out scheduling actions in a continuous latent space with a lightweight transition model, avoiding expensive AST mutation and repeated code encoding. The final dynamic representation is combined with action and hardware features to rank candidates. Implemented in TVM AutoScheduler, our method improves representative-subgraph latency over Ansor by 1.37$\times$ on GPU and 1.54$\times$ on CPU under the same 64-trial budget. It also matches Ansor-10K within 2.2% geometric mean using 10$\times$ fewer measurements, and accelerates full-model inference over PyTorch/PyTorch-opt(cuDNN) by 4.61$\times$/3.67$\times$ geometric mean.