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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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