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README.md

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**Vision**: We aim to achieve cross-hardware portability of compiler optimizations by allowing models to learn and transfer optimization strategies. It will significantly reduce the manual effort required to develop efficient operator implementations.
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### Dataset Construction Constraints:
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1. Dynamic graphs must execute correctly.
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2. Each computation graph should include a standardized method for measuring performance.
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3. Graphs and their corresponding Python code must support serialization and deserialization.
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4. The full graph can be decomposed into two disjoint subgraphs.
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5. Compiler passes or behaviors must be configurable.
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6. Operator names within each computation graph must be statically parseable.
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7. If custom operators are used, their implementation code must be fully accessible.
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8. Graph execution on different hardware backends must be configurable via a unified interface.
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2. Graphs and their corresponding Python code must support serialization and deserialization.
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3. The full graph can be decomposed into two disjoint subgraphs.
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4. Operator names within each computation graph must be statically parseable.
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5. If custom operators are used, their implementation code must be fully accessible.
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## ⚡ Quick Start
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<table>
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<tr>
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<td align="center">
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<img width="200" src="https://github.com/user-attachments/assets/3ea4794b-1aed-4096-bd1c-3184832b98f3" />
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<img width="200" src="https://github.com/user-attachments/assets/125e3494-25c9-4494-9acd-8ad65ca85d03" />
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</td>
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<td align="center">
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<img width="150" src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/67d00cf7266d2c75571aebde_Example.svg" />
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<p><a href="https://discord.gg/FCZQVCkC">Channel</a> is also available.</p>
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<p><a href="https://discord.gg/vyeAydwh">Channel</a> is also available.</p>
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</td>
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</tr>
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</table>
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</div>
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## License
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This project is released under the [MIT License](LICENSE).
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README_cn.md

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