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This paper introduces TENSURE, a black-box fuzzing framework tailored for Sparse Tensor Compilers (STCs) that uses Einstein Summation notation to generate complex tensor contractions. TENSURE employs a constraint-based generation algorithm to ensure 100% semantic validity of synthesized kernels and metamorphic testing using semantic-preserving mutations. Evaluation on TACO and Finch revealed significant fragility, with TENSURE exposing crashes or miscompilations in TACO for a majority of generated test cases.
Most sparse tensor compilers are riddled with bugs, silently miscompiling code or crashing on valid inputs, a problem exposed by a new fuzzer that guarantees valid tensor contractions.
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.
