Steve Zdancewic

Vadim Zaliva, Yannick Zakowski, Ilia Zaichuk et al.

This paper presents the design of HELIX, an end-to-end verified code generation system with a focus on the intersection of high-performance and high-assurance numerical computing. The code generation can be fine-tuned to generate efficient code for a broad set of computer architectures while providing formal guarantees of the correctness of such generated code. Using a real-life example of a cyber-physical robot system, this paper demonstrates how, by using HELIX, one can start from a high-level mathematical formulation of the problem, apply a series of algebraic transformations that target intermediate languages, and generate an efficient imperative implementation. This is done while formally verifying semantic preservation from the original formulation down to LLVM IR. The method we used for high-performance code compilation is the algebraic transformation of vector and matrix computations into a dataflow optimised for parallel or vectorised processing on target hardware. The abstraction used to formalise and verify this technique is an operator language and accompanying semantics-preserving term rewriting. We use sparse vector abstraction to represent partial computations, enabling us to use algebraic reasoning to prove parallel decomposition properties. HELIX's verification infrastructure comprises multiple intermediate languages and verification approaches, all implemented in the Coq proof assistant. In particular, it uses verified term rewriting, translation validation, metaprogramming, verified compilation, layered monadic interpreters; it also supports application-specific uses of (verified) numerical analysis as we demonstrate via the running example.

George Tolkachev, Stephen Mell, Steve Zdancewic et al.

A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system. We propose a novel approach for generating explanations of a natural language interface based on semantic parsing. We focus on counterfactual explanations, which are post-hoc explanations that describe to the user how they could have minimally modified their utterance to achieve their desired goal. In particular, the user provides an utterance along with a demonstration of their desired goal; then, our algorithm synthesizes a paraphrase of their utterance that is guaranteed to achieve their goal. In two user studies, we demonstrate that our approach substantially improves user performance, and that it generates explanations that more closely match the user's intent compared to two ablations.

Stephen Mell, David Mell, Konstantinos Kallas et al.

Compound AI applications, which compose calls to ML models using a general-purpose programming language like Python, are widely used for a variety of user-facing tasks, from software engineering to enterprise automation, making their end-to-end latency a critical bottleneck. In contrast to traditional applications, execution time is dominated by the external components, which cannot be handled by traditional language optimization systems, like optimizing compilers. To address this problem, we develop PopPy, a system that can uncover parallelization opportunities in Python applications that invoke these heavy external components, including those used in compound AI applications. PopPy supports a very expressive fragment of Python and requires minimal developer input to uncover parallelism. It combines an ahead-of-time compiler with a runtime, addressing three key challenges in extracting parallelism from Python applications: language complexity, dynamic dispatch, and variable mutation. On a set of real-world compound AI applications, PopPy achieves up to $6.4\times$ speedups in end-to-end execution time compared to standard Python execution while preserving the sequential program semantics.

Stephen Mell, Botong Zhang, David Mell et al.

Modern large language models (LLMs) are often deployed as agents, calling external tools adaptively to solve tasks. Rather than directly calling tools, it can be more effective for LLMs to write code to perform the tool calls, enabling them to automatically generate complex control flow such as conditionals and loops. Such code actions are typically provided as Python code, since LLMs are quite proficient at it; however, Python may not be the ideal language due to limited built-in support for performance, security, and reliability. We propose a novel programming language for code actions, called Quasar, which has several benefits: (1) automated parallelization to improve performance, (2) uncertainty quantification to improve reliability and mitigate hallucinations, and (3) security features enabling the user to validate actions. LLMs can write code in a subset of Python, which is automatically transpiled to Quasar. We evaluate our approach on the ViperGPT visual question answering agent, applied to the GQA dataset, demonstrating that LLMs with Quasar actions instead of Python actions retain strong performance, while reducing execution time when possible by 42%, improving security by reducing user approval interactions when possible by 52%, and improving reliability by applying conformal prediction to achieve a desired target coverage level.