LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track)
Bibliographic record
Abstract
As machine learning (ML) has become more preva-lent across many critical domains, so has the need to understand ML applications' resilience. While prior work like TensorFI [1], MindFI [2], and PyTorchFI [3] has focused on building ML fault injectors for specific ML frameworks, there has been little work on performing fault injection (FI) for ML applications written in multiple frameworks. We present LLTFI, a framework-agnostic fault injection tool for ML applications, allowing users to run FI experiments on ML applications at the LLVM IR level. LLTFI provides users with finer FI granularity at the level of instructions, and a better understanding of how faults manifest and propagate between different ML components. We evaluate LLTFI on six ML programs and compare it with TensorFI. We found significant differences in the Silent Data Corruption (SDC) rates for similar faults between the two tools. Finally, we use LLTFI to evaluate the efficacy of selective instruction duplication - an error mitigation technique - for ML programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".