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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.19705 |
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| _version_ | 1866912350073782272 |
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| author | Li, Yixuan Magalhães, José Wesley de Souza Brauckmann, Alexander O'Boyle, Michael F. P. Polgreen, Elizabeth |
| author_facet | Li, Yixuan Magalhães, José Wesley de Souza Brauckmann, Alexander O'Boyle, Michael F. P. Polgreen, Elizabeth |
| contents | Domain-specific languages (DSLs) for machine learning are revolutionizing the speed and efficiency of machine learning workloads as they enable users easy access to high-performance compiler optimizations and accelerators. However, to take advantage of these capabilities, a user must first translate their legacy code from the language it is currently written in, into the new DSL. The process of automatically lifting code into these DSLs has been identified by several recent works, which propose program synthesis as a solution. However, synthesis is expensive and struggles to scale without carefully designed and hard-wired heuristics. In this paper, we present an approach for lifting that combines an enumerative synthesis approach with a Large Language Model used to automatically learn the domain-specific heuristics for program lifting, in the form of a probabilistic grammar. Our approach outperforms the state-of-the-art tools in this area, despite only using learned heuristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guided Tensor Lifting Li, Yixuan Magalhães, José Wesley de Souza Brauckmann, Alexander O'Boyle, Michael F. P. Polgreen, Elizabeth Software Engineering Domain-specific languages (DSLs) for machine learning are revolutionizing the speed and efficiency of machine learning workloads as they enable users easy access to high-performance compiler optimizations and accelerators. However, to take advantage of these capabilities, a user must first translate their legacy code from the language it is currently written in, into the new DSL. The process of automatically lifting code into these DSLs has been identified by several recent works, which propose program synthesis as a solution. However, synthesis is expensive and struggles to scale without carefully designed and hard-wired heuristics. In this paper, we present an approach for lifting that combines an enumerative synthesis approach with a Large Language Model used to automatically learn the domain-specific heuristics for program lifting, in the form of a probabilistic grammar. Our approach outperforms the state-of-the-art tools in this area, despite only using learned heuristics. |
| title | Guided Tensor Lifting |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2504.19705 |