5. AI Compiler Frontend

Tailored for machine learning frameworks, an AI compiler is designed to convert Python-based machine learning programs into their optimized forms, enabling efficient native execution on heterogeneous processors. This chapter first outlines the typical architecture of an AI compiler before delving into the design of the compiler’s frontend. The compiler frontend incorporates various techniques, including intermediate representations (IRs), automatic differentiation, type systems, static analysis, and compilation optimization.

The learning objectives of this chapter include:

• Understanding the typical architecture of an AI compiler.

• Understanding the types and implementation of IRs in machine learning frameworks.

• Understanding the methods of automatic differentiation implemented in AI compilers.

• Understanding type systems and static analysis in AI compilers.

• Understanding common frontend compilation optimization methods used by AI compilers.

• 5.1. Overview of AI Compilers
• 5.2. Overview of AI Compiler Frontends
• 5.3. Intermediate Representation
  • 5.3.1. Definition of Intermediate Representations
  • 5.3.2. Types of Intermediate Representations
  • 5.3.3. Intermediate Representation in Machine Learning Frameworks
• 5.4. Automatic Differentiation
  • 5.4.1. Types of Differentiation Methods
  • 5.4.2. Forward Mode and Reverse Mode
  • 5.4.3. Implementing Automatic Differentiation
• 5.5. Type Systems and Static Analysis
  • 5.5.1. Type Systems
  • 5.5.2. Static Analysis
• 5.6. Frontend Compilation Optimization
  • 5.6.1. Process of Compilation Optimization
  • 5.6.2. Prevalent Optimization Methods
• 5.7. Chapter Summary
• 5.8. Further Reading