MLIR-Forge: A Modular Framework for Language Smiths

2026-01-14

AI Summary

This work proposes a modular framework for random program generation targeting compiler intermediate representations (IRs), addressing the high complexity and effort traditionally required to develop such generators. By decoupling language-specific constructs from generic generation logic, the framework significantly lowers the barrier to implementing new IR-aware generators. Built upon the MLIR infrastructure, it integrates modular design, randomized program synthesis, differential testing, and fuzzing to enable rapid adaptation across diverse IRs. The authors demonstrate the approach’s effectiveness and generality by developing generators for MLIR’s built-in dialects, WebAssembly, and DaCe within a single week, uncovering 9, 15, and 774 distinct bug-triggering test cases, respectively. These results substantiate the framework’s capacity to facilitate efficient and scalable compiler testing.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Logic Programming

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Optimizing compilers are essential for the efficient and correct execution of software across various scientific fields. Domain-specific languages (DSL) typically use higher level intermediate representations (IR) in their compiler pipelines for domain-specific optimizations. As these IRs add to complexity, it is crucial to test them thoroughly. Random program generators have proven to be an effective tool to test compilers through differential and fuzz testing. However, developing specialized program generators for compiler IRs is not straightforward and demands considerable resources. We introduce MLIR-Forge, a novel random program generator framework that leverages the flexibility of MLIR, aiming to simplify the creation of specialized program generators. MLIR-Forge achieves this by splitting the generation process into fundamental building blocks that are language specific, and reusable program creation logic that constructs random programs from these building blocks. This hides complexity and furthermore, even the language specific components can be defined using a set of common tools. We demonstrate MLIR-Forge's capabilities by generating MLIR with built-in dialects, WebAssembly, and a data-centric program representation, DaCe -- requiring less than a week of development time in total for each of them. Using the generated programs we conduct differential testing and find 9 MLIR, 15 WebAssembly, and 774 DaCe groups of bugs with the corresponding program generators, after running them until the rate of new bugs stagnates.

Problem

• random program generation
• compiler testing
• intermediate representation
• domain-specific languages
• fuzz testing

Innovation

• MLIR-Forge
• random program generation
• modular framework
• differential testing
• compiler validation