Enhancing Compiler Design for Machine Learning Workflows with MLIR

The rapidly changing world of machine learning (ML) workloads has added a tremendous burden on our conventional compiler infrastructures, and these systems frequently lack the flexibility, scalability, and optimization options needed to address the emerging, heterogeneous computing landscape. As ML frameworks continue to expand their support to a wide variety of hardware platforms (such as GPUs, TPUs, or FPGAs), it is critical that the corresponding compiler infrastructures accommodate the now-requisite levels of both high-level algorithmic abstractions and low-level hardware-specific optimizations. Google has developed the Multi-Level Intermediate Representation, the Multi-Level, which solves these problems by delivering a modular, extensible compiler infrastructure, optimized to the modern ML development workflow. MLIR facilitates the specification of domain-specific intermediate representations (IRs) and thus allows optimizations at many abstraction levels, including tensor algebra to hardware-specific instruction sets. This architecture enables control of code transformation at the finest grain, enables custom dialects, and has encouraged interoperability among popular ML frameworks (TensorFlow, PyTorch, JAX, and ONNX). Using MLIR as a compiler design aspect, developers gain both better portable compilation and higher optimization granularity, as well as time-reducing development. In addition, MLIR progresses in the realization of AI-specific compiler optimizations, including quantization, operator fusion, and parallel execution scheduling. Finally, MLIR is an important step in compiler design that will not only deliver more performance on ML applications, but also a more maintainable and extensible ecosystem for future AI-driven computing systems. This paper discusses the new paradigm offered by MLIR, its ability to redefine compiler infrastructure to satisfy the sophisticated needs of present ML workloads, and the avenue to more intelligent, flexible, and resource-efficient software-hardware integration.