An MLIR-Based Compilation Method for Large Language Models
Large Language Models (LLMs) have become the dominant workload on modern AI accelerators, yet deploying them on specialized hardware still faces two core challenges: how to import a trained model into a compiler-friendly intermediate representation, and how to efficiently schedule the autoregressive inference loop under limited on-chip memory. This paper presents an MLIR (Multi-Level Intermediate Representation) based compilation method for large language models, illustrated using two dialects of operators, TopOp and TpuOp. TopOp serves as a high-level graph dialect that is independent of both
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Pengchao Hu →
“An MLIR-Based Compilation Method for Large Language Models”
- LinkedLinked via arxiv author · 85%Zhibin Xin →
“An MLIR-Based Compilation Method for Large Language Models”
- LinkedLinked via arxiv author · 85%Yifan Chen →
“An MLIR-Based Compilation Method for Large Language Models”
- LinkedLinked via arxiv author · 85%Yangyang Zhou →
“An MLIR-Based Compilation Method for Large Language Models”
- LinkedLinked via arxiv author · 85%Dongliang Wang →
“An MLIR-Based Compilation Method for Large Language Models”