New Machine Learning Method Improves Tensor Program Optimization for AI Systems
A team of researchers has introduced a new approach to tensor program optimization that models scheduling as action-conditioned latent dynamics, rather than evaluating each candidate as a static code snapshot. The method, implemented within TVM AutoScheduler, outperforms the widely used Ansor auto-scheduler by 1.37× on GPU and 1.54× on CPU under the same 64-trial measurement budget. The work addresses a key inefficiency in machine learning compiler search, potentially reducing the cost and time required to optimize neural network inference.
Tensor program optimization is a critical step in deploying machine learning models efficiently, but the search space of possible schedules is extremely large, making exhaustive evaluation impractical. Existing auto-schedulers like Ansor use learned cost models to reduce the number of real hardware measurements needed, but they evaluate each candidate program as a static snapshot, ignoring the sequence of scheduling actions that produced it. The proposed 'compiler world model' addresses this by rolling out scheduling actions in a continuous latent space using a lightweight transition model, avoiding costly abstract syntax tree (AST) mutations and repeated code encoding. The final representation combines dynamic program state with action and hardware features to rank scheduling candidates. Benchmarks show the method matches Ansor's 10,000-trial performance within 2.2% geometric mean using only 1,000 trials—a 10× reduction in measurements—and accelerates full-model inference by 4.61× over standard PyTorch and 3.67× over PyTorch with cuDNN optimizations. The work was submitted to arXiv in June 2026 and has not yet undergone formal peer review.
What's missing
The study has not yet been peer-reviewed, as it is a preprint. Evaluations appear limited to specific GPU and CPU hardware configurations; generalizability to other hardware backends (e.g., mobile accelerators, TPUs) is not established. Comparisons beyond Ansor—such as against more recent auto-schedulers or learned search methods—are not reported, leaving open questions about performance relative to the broader state of the art.
What different sources said
- arXiv cs.LGCenter
Toward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search
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