TENSURE: Fuzzing Sparse Tensor Compilers (Registered Report)

TL;DR

TENSURE is a novel fuzzing framework that effectively tests sparse tensor compilers by generating valid, complex tensor contractions, revealing widespread bugs and emphasizing the need for specialized testing tools.

Contribution

It introduces a constraint-based, semantic-validity-guaranteeing fuzzing approach for sparse tensor compilers using Einsum notation and mutation operators.

Findings

Exposed crashes and miscompilations in TACO and Finch.

Achieved 100% validity in generated kernels, outperforming baseline methods.

Revealed widespread fragility in state-of-the-art sparse tensor systems.

Abstract

Sparse Tensor Compilers (STCs) have emerged as critical infrastructure for optimizing high-dimensional data analytics and machine learning workloads. The STCs must synthesize complex, irregular control flow for various compressed storage formats directly from high-level declarative specifications, thereby making them highly susceptible to subtle correctness defects. Existing testing frameworks, which rely on mutating computation graphs restricted to a standard vocabulary of operators, fail to exercise the arbitrary loop synthesis capabilities of these compilers. Furthermore, generic grammar-based fuzzers struggle to generate valid inputs due to the strict rules governing how indices are reused across multiple tensors. In this paper, we present TENSURE, the first extensible black-box fuzzing framework specifically designed for the testing of STCs. TENSURE leverages Einstein Summation (Einsum) notation as a general input abstraction, enabling the generation of complex, unconventional tensor contractions that expose corner cases in the code-generation phases of STCs. We propose a novel constraint-based generation algorithm that guarantees 100% semantic validity of synthesized kernels, significantly outperforming the ~3.3% validity rate of baseline grammar fuzzers. To enable metamorphic testing without a trusted reference, we introduce a set of semantic-preserving mutation operators that exploit algebraic commutativity and heterogeneity in storage formats. Our evaluation on two state-of-the-art systems, TACO and Finch, reveals widespread fragility, particularly in TACO, where TENSURE exposed crashes or silent miscompilations in a majority of generated test cases. These findings underscore the critical need for specialized testing tools in the sparse compilation ecosystem.